best phd for machine learning

Best Universities for Machine Learning in the World

Updated: February 29, 2024

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Below is a list of best universities in the World ranked based on their research performance in Machine Learning. A graph of 165M citations received by 7.75M academic papers made by 5,307 universities in the World was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Stanford University

For Machine Learning

2. University of California - Berkeley

3. Harvard University

4. University of Michigan - Ann Arbor

5. University of Toronto

6. University of Washington - Seattle

7. Carnegie Mellon University

8. Massachusetts Institute of Technology

9. Tsinghua University

10. University of Illinois at Urbana - Champaign

11. University of Oxford

12. University of California - Los Angeles

13. Cornell University

14. University College London

15. University of Minnesota - Twin Cities

16. Johns Hopkins University

17. Nanyang Technological University

18. University of California-San Diego

19. University of Wisconsin - Madison

20. National University of Singapore

21. Pennsylvania State University

22. University of Pennsylvania

23. University of Cambridge

24. Columbia University

25. Shanghai Jiao Tong University

26. University of Southern California

27. New York University

28. University of Texas at Austin

29. University of Hong Kong

30. Yale University

31. Georgia Institute of Technology

32. University of Maryland - College Park

33. Ohio State University

34. Imperial College London

35. Catholic University of Leuven

36. Princeton University

37. University of North Carolina at Chapel Hill

38. University of British Columbia

39. University of Chicago

40. Harbin Institute of Technology

41. Peking University

42. Arizona State University - Tempe

43. Huazhong University of Science and Technology

44. Michigan State University

45. University of Sydney

46. Zhejiang University

47. Duke University

48. Swiss Federal Institute of Technology Zurich

49. Texas A&M University - College Station

50. Technical University of Munich

51. University of Florida

52. Chinese University of Hong Kong

53. University of Melbourne

54. University of Alberta

55. University of New South Wales

56. University of Amsterdam

57. University of Tokyo

58. Federal Institute of Technology Lausanne

59. University of Edinburgh

60. Rutgers University - New Brunswick

61. University of Pittsburgh

62. Xi'an Jiaotong University

63. Purdue University

64. Beihang University

65. University of Waterloo

66. University of Electronic Science and Technology of China

67. Boston University

68. Hong Kong Polytechnic University

69. University of California - Davis

70. University of Manchester

71. McGill University

72. Wuhan University

73. Northwestern University

74. National Taiwan University

75. University of California - Irvine

76. Southeast University

77. University of Montreal

78. Central South University

79. University of Queensland

80. Delft University of Technology

81. Seoul National University

82. Iowa State University

83. Sun Yat - Sen University

84. University of Arizona

85. Monash University

86. University of California - San Francisco

87. University of Science and Technology of China

88. Northwestern Polytechnical University

89. Virginia Polytechnic Institute and State University

90. City University of Hong Kong

91. University of Sao Paulo

92. University of Massachusetts - Amherst

93. University of Bristol

94. University of Sheffield

95. Australian National University

96. California Institute of Technology

97. Hong Kong University of Science and Technology

98. Dalian University of Technology

99. North Carolina State University at Raleigh

100. Xidian University

Computer Science subfields in the World

Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Graduate Education

Office of graduate and postdoctoral education, machine learning (ml), program contact.

Stephanie Niebuhr Georgia Institute of Technology 801 Atlantic Drive Atlanta, GA 30332-0405

Application Deadlines

Application deadline varies by home school.

• Aerospace Engineering: March 3
• Biomedical Engineering: December 1
• Electrical and Computer Engineering: December 16
• Industrial & Systems Engineering: December 15
• Mathematics: December 15
• School of Chemical & Biomolecular Engineering: December 15
• School of Computational Science & Engineering: December 15
• School of Computer Science: December 15
• School of Interactive Computing: December 15

Admittance Terms

Degree programs.

• PhD, Machine Learning

Areas of Research

Our world-class faculty and students specialize in areas including, but not limited to:

• Computer Vision
• Natural Language Processing
• Deep Learning
• Game Theory
• Neuro Computing
• Ethics and Fairness
• Artificial Intelligence
• Internet of Things
• Machine Learning Theory
• Systems for Machine Learning
• Bioinformatics
• Computational Finance
• Health Systems
• Information Security
• Logistics and Manufacturing

Interdisciplinary Programs

The Machine Learning Ph.D. is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of eight participating home schools:

• Computer Science (Computing)
• Computational Science and Engineering (Computing)
• Interactive Computing (Computing)– see  Computer Science
• Aerospace Engineering (Engineering)
• Biomedical Engineering (Engineering)
• Electrical and Computer Engineering (Engineering)
• Mathematics (Sciences)
• Industrial Systems Engineering (Engineering)

Admission to the ML PhD program is contingent on meeting the requirement for admission into one of these schools. It is possible that, due to space or other constraints, that you are admitted to the general PhD program in your home school but not the ML PhD program.

The ML PhD program is a cohesive, interdisciplinary course of study subject to a unique set of curriculum requirements; see the program webpage for more information.

Standardized Tests

IELTS Academic Requirements

• Varies among home units.

TOEFL Requirements

GRE Requirements

Application Requirements

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships). Please review the home unit links above or contact them directly for details.

Program Costs

• Go to " View Tuition Costs by Semester ," and select the semester you plan to start. Graduate-level programs are divided into sections: Graduate Rates–Atlanta Campus, Study Abroad, Specialty Graduate Programs, Executive Education Programs
• Find the degree and program you are interested in and click to access the program's tuition and fees by credit hour PDF.
• In the first column, determine the number of hours (or credits) you intend to take for your first semester.
• Determine if you will pay in-state or out-of-state tuition. Learn more about the difference between in-state and out-of-state . For example, if you are an in-state resident and planning to take six credits for the Master of Architecture degree, the tuition cost will be $4,518.
• The middle section of the document lists all mandatory Institute fees. To see your total tuition plus mandatory fees, refer to the last two columns of the PDF.

Program Links

The Office of Graduate Education has prepared an admissions checklist to help you navigate through the admissions process.

College of Computing

Ph.d. in machine learning, about the curriculum.

The central goal of the Ph.D. program is to train students to perform original, independent research. The most important part of the curriculum is the successful defense of a Ph.D. dissertation, which demonstrates this research ability.

The curriculum is designed with the following principal educational goals:

•    Students will develop a solid understanding of fundamental principles across a range of core areas in the machine learning discipline. •    Students will develop a deep understanding and set of skills and expertise in a specific theoretical aspect or application area of the machine learning discipline. •    The students will be able to apply and integrate the knowledge and skills they have developed and demonstrate their expertise and proficiency in an application area of practical importance. •    Students will be able to engage in multidisciplinary activities by being able to communicate complex ideas in their area of expertise to individuals in other fields, be able to understand complex ideas and concepts from other disciplines, and be able to incorporate these concepts into their own work. The curriculum for the Ph.D. in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech:  •    Computer Science (Computing) •    Computational Science and Engineering (Computing) •    Interactive Computing (Computing) – see Computer Science •     Aerospace Engineering (Engineering) •     Biomedical Engineering (Engineering) •     Electrical and Computer Engineering (Engineering) •     Industrial Systems Engineering (Engineering) •     Mathematics (Sciences) Students must complete four core courses, five electives, a qualifying exam, and a doctoral dissertation defense. All doctorate students are advised by ML Ph.D. Program Faculty . All coursework must be completed before the Ph.D. proposal. An overall GPA of 3.0 is required for the Ph.D. coursework.

Research Opportunities

Our faculty comes from all six colleges across Georgia Tech’s campus, creating many interdisciplinary research opportunities for our students. Our labs focus on research areas such as artificial intelligence, data science, computer vision, natural language processing, optimization, machine learning theory, forecasting, robotics, computational biology, fintech, and more.

External applications are only accepted for the Fall semester each year. The application deadline varies by home school. 

The Machine Learning Ph.D. admissions process works bottom-up through the home schools. Admissions decisions are made by the home school, and then submitted to the Machine Learning Faculty Advisory Committee (FAC) for final approval. Support for incoming students (including guarantees of teaching assistantships and/or fellowships) is determined by the home schools. 

After the admissions have been approved by the FAC, the home school will communicate the acceptance to the prospective student. The home school will also communicate all rejections.

Get to Know Current ML@GT Students

Learn more about our current students, their interests inside and outside of the lab, favorite study spots, and more.

Career Outlook

The machine learning doctorate degree prepares students for a variety of positions in industry, government, and academia. These positions include research, development, product managers, and entrepreneurs. 

Graduates are well prepared for position in industry in areas such as internet companies, robotic and manufacturing companies and financial engineering, to mention a few. Positions in government and with government contractors in software and systems are also possible career paths for program graduates. Graduates are also well-suited for positions in academia involving research and education in departments concerned with the development and application of data-driven models in engineering, the sciences, and computing. 

Frequently Asked Questions

For additional questions regarding the ML Ph.D. program, please take a look at our frequently asked questions.

You can also view the ML Handbook which has detailed information on the program and requirements.

From the Catalog:

Carnegie Mellon University School of Computer Science

Machine learning department.

Ph.D. in Machine Learning

Machine learning is dedicated to furthering scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision-making based on that understanding. The doctoral program in machine learning trains students to become tomorrow's leaders in this rapidly growing area.

Joint Ph.D. in Machine Learning and Public Policy

The Joint Ph.D. Program in Machine Learning and Public Policy is a new program for students to gain the skills necessary to develop state-of-the-art machine learning technologies and apply these technologies to real-world policy issues.

Joint Ph.D. in Neural Computation and Machine Learning

This Ph.D. program trains students in the application of machine learning to neuroscience by combining core elements of the machine learning Ph.D. program and the Ph.D. in neural computation offered by the Center for the Neural Basis of Cognition.

Joint Ph.D. in Statistics and Machine Learning

This joint program prepares students for academic careers in both computer science and statistics departments at top universities. Students in this track will be involved in courses and research from both the Department of Statistics and the Machine Learning Department.

Visit the Website

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Top 10 AI graduate degree programs

Thinking about getting your graduate degree in artificial intelligence here are 10 of the top schools with ai degrees worth pursuing..

Artificial Intelligence (AI) is a fast-growing and evolving field, and data scientists with AI skills are in high demand. The field requires broad training involving principles of computer science, cognitive psychology, and engineering. If you want to grow your data scientist career and capitalize on the demand for the role, you might consider getting a graduate degree in AI.

U.S. News & World Report ranks the best AI graduate programs at computer science schools based on surveys sent to academic officials in fall 2022 and early 2023 in chemistry, computer science, earth science, mathematics, and physics.

Here are the top 10 programs that made the list that have the best AI graduate programs in the US.

1. Carnegie Mellon University

The Machine Learning Department of the School of Computer Science at Carnegie Mellon University was founded in 2006 and grew out of the Center for Automated Learning and Discovery (CALD), itself created in 1997 as an interdisciplinary group of researchers with interests in statistics and machine learning. CALD drew from the Statistics Department and departments within the School of Computer Science, as well as faculty from philosophy, engineering, the business school, and biological science.

Carnegie Mellon says the department’s research strategy is to maintain a balance between research into the cure statistical-computational theory of machine learning, and research inventing new algorithms and new problem formulations relevant to practical applications.

The Machine Learning Department offers both doctoral and master’s programs in machine learning, including:

• PhD in Machine Learning (ML)
• Joint PhD Program in Statistics & Machine Learning (offered jointly with the Statistics Department)
• Joint PhD Program in Machine Learning & Public Policy (offered jointly with the Heinz College Schools of Public Policy, Information Systems, and Management)
• Joint PhD Program in Neural Computation & Machine Learning (offered jointly with the Neuroscience Institute)
• Primary Master’s in Machine Learning
• 5th-Year Master’s in Machine Learning (a one-year program for current CMU students)
• Secondary Master’s in Machine Learning (for current CMU PhD students, faculty, or staff)

2. Massachusetts Institute of Technology (MIT)

The MIT Department of Electrical Engineering and Computer Science (EECS) is the largest academic department at MIT. A joint venture with the MIT Schwarzman College of Computing offers three overlapping sub-units in electrical engineering (EE), computer science (CS), and artificial intelligence and decision-making (AI+D).

MIT says AI+D’s research explores the foundations of machine learning and decision systems (AI, reinforcement learning, statistics, causal inference, systems, and control), the building blocks of embodied intelligence ( computer vision , NLP , robotics), applications to real-world autonomous systems, life sciences, and the interface between data-driven decision-making and society.

The EECS Department graduate degree programs include:

• Master of Science (MS), which is required of students pursuing a doctoral degree
• Master of Engineering (MEng), for MIT EECS undergraduates
• Electrical Engineer (EE)/Engineer in Computer Science (ECS)
• Doctor of Philosophy (PhD)/Doctor of Science (ScD), awarded interchangeably

3. Stanford University

Stanford University’s Computer Science Department is part of the School of Engineering . The Stanford AI Lab (SAIL) was founded in 1962 as a center of excellence for AI research, teaching, theory, and practice. In addition to its in-person programs, Stanford Online offers the Artificial Intelligence Graduate Certificate entirely online. The AI program focuses on the principles and technologies that underlie AI, including logic, knowledge representation, probabilistic models, and machine learning.

Stanford offers both PhDs and an MSCS with an AI specialization.

4. University of California – Berkeley

The University of California – Berkeley Department of Electrical Engineering and Computer Sciences focuses its foundational research in core areas of deep learning, knowledge representation, reasoning, learning, planning, decision-making, vision, robotics, speech, and NLP. There are also efforts to apply algorithmic advances to applied problems in a range of areas, including bioinformatics, networking and systems, search, and information retrieval. It’s closely associated with the Berkeley Artificial Intelligence Research (BAIR) Lab.

Berkeley offers both PhDs and master’s programs.

5. University of Illinois – Urbana-Champaign

The University of Illinois – Urbana-Champaign Grainger College of Engineering focuses its AI and machine learning program on computer vision, machine listening, NLP, and machine learning. In computer vision, the AI group faculty are developing novel approaches for 2D and 3D scene understanding from still images and video, low-shot learning, and more. The machine listening faculty is working on sound and speech understanding, source separation, and applications in music and computing. The machine learning faculty studies the theoretical foundations of deep and reinforcement learning; develops novel models and algorithms for deep neural networks, federated, and distributed learning; and addresses issues related to scalability, security, privacy, and fairness of learning systems.

The university offers a CS PhD program, CS MS program, a professional master’s of computer science program, and a fifth-year master’s program.

6. Georgia Institute of Technology

Georgia Tech College of Computing says AI and machine learning represent a large swath of its faculty and research interests, including constructing top-to-bottom and bottom-to-top models of human-level intelligence; building systems that can provide intelligent tutoring; creating adaptive and intelligent entertainment systems; making systems that understand their own behavior; and constructing autonomous agents that can adapt in dynamic environments.

Different groups within the school emphasize different areas of research. The core faculty comes from the School of Interactive Computing, but there are also machine learning faculty in the schools of Computer Science and Computational Science & Engineering.

Georgia Tech offers both master’s and doctoral programs, including a PhD in Machine Learning.

7. University of Washington

The University of Washington Paul G. Allen School of Computer Science & Engineering offers an AI group that studies the computational mechanisms underlying intelligent behavior. Research areas include machine learning, NLP, probabilistic reasoning, automated planning, machine reading, and intelligent user interfaces. It collaborates closely with the Allen Institute for Artificial Intelligence (AI2).

The University of Washington offers a combined bachelor’s of science (BS)/master’s of science (MS) program created with industry-bound students in mind, a full-time PhD program, a professional master’s program (a part-time, evening program), and a postdoctoral research program.

8. University of Texas – Austin

The University of Texas at Austin Department of Computer Science is focused on computer vision, evolutionary computation, machine learning, multimodality, NLP, neural networks, reinforcement learning, and robotics. It hosts myriad research centers and labs, including the Laboratory for Artificial Intelligence, which opened in 1983 and investigates the central challenges of machine cognition, including machine learning, knowledge representation, and reasoning. Some others include the Institute for Foundations of Machine Learning, Machine Learning Lab, Machine Learning Research Group, and Neural Networks Research Group.

The University of Texas offers a PhD program, master’s program, online master’s program in computer science, online master’s program in data science, and five-year BS/MS programs.

9. Cornell University

Cornell Bowers CIS College of Computing and Information Science has been building out its AI group since the 1990s. In 2021, it launched a new initiative, a new Radical Collaboration , laid out by scholars across the university to advance its reputation as a leader in AI research, education, and ethics. The initiative expands faculty working in core areas and other domains affected by AI advances. Recent interdisciplinary collaborations across the Ithaca Campus, Cornell Tech, and Weill Cornell Medicine have applied AI to issues ranging from sustainable agriculture and urban design to cancer detection, improving autonomous vehicles, and parsing quantum matter.

Cornell offers a Master of Engineering in Computer Science program, as well as a Computer Science Master’s of Science program, and PhD program.

10. University of Michigan – Ann Arbor

The University of Michigan Computer Science and Engineering division offers an AI program comprised of multidisciplinary researchers studying rational decision making, distributed systems of multiple agents, machine learning, reinforcement learning, cognitive modeling, game theory, NLP, machine perception, healthcare computing, and robotics.

The university says research in the AI laboratory tends to be highly interdisciplinary, building on ideas from computer science, linguistics, psychology, economics, biology, controls, statistics, and philosophy.

The University of Michigan offers a PhD in CSE, master’s in CSE, and master’s in data science.

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Georgia Institute of Technology

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• PhD Program

The PhD in Machine Learning is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of nine participating home schools:

• Contact SCS
• Contact CSE
• Contact ChBE
• Contact BME
• Contact ECE
• Contact ISYE
• ​​​​​​​ Contact MATH

Application requirements and deadlines follow the same as that of the home unit an applicant is applying through. For example, ML PhD applicants to the ECE home unit follow the same rules as the PhD ECE application requirements and deadlines. 

External applications are only accepted for the Fall semester each year.  The application deadline varies by home school with the earliest deadline of December 1. Most home schools have a final deadline of December 15. Check with home schools above for more specific details. 

Click here for application information and to apply  

Applicants must meet all admissions standards (including requirements on the minimum GPA, minimum GRE/TOEFL scores) of the home unit, which may vary. After an initial review, the unit’s representative of the ML Ph.D. Faculty Advisory Committee (FAC) will submit their candidates for review and the final admission decision will be made by the ML FAC.

Note most home units have made the GRE optional for fall 2023 applications. Contact the home unit at the above links for any specific info. 

The committee’s decision to admit will be based on (1) prior academic performance of the applicant in a B.S. or M.S. program at a recognized institution, including coursework and independent research projects, (2) prior work experience relevant to ML, (3) the applicant’s statement of purpose, and (4) the letters of support.

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships) are determined by the home units. Please review the home unit links above or contact them directly for details.

Have Questions?

Please contact the above  home units directly for questions related to:.

• Application deadlines
• Application fee waivers
• Assistantship/fellowship opportunities
• Program fit
• Advising Matching
• GRE requirements - Many units have made this test optional. 
• TOEFL minimum requirements and TOEFL waivers are determined by the GT Graduate Education Office:  https://grad.gatech.edu/english-proficiency . Note home units may required higher scores. 
• Desired content in Statement of Purpose and Recommendation Letters

For technical application questions, please contact  [email protected]: 

• Creating or using an account login
• Forgotten password
• Uploading documents 
• Difficulty with recommender emails
• How to access application status information (including application checklist)
• Difficulty with the touchnet payment system

For general inquiries about curriculum or program requirements, please see FAQs or contact [email protected] .

Georgia Tech Transfer Students

If you are already enrolled in a Ph.D. program in one of the nine participating schools noted above, you may apply to the ML Ph.D. program as a transfer student.  You will be subject to the standard ML curriculum and qualifying requirements, so this is recommended only for graduate students in their first or second year.  

Potential transfer students must have a ML PhD Program thesis adviso r  who is willing to support them on a research assistantship. For more information, please email [email protected] .

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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

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Online Doctor of Engineering in Artificial Intelligence & Machine Learning

We are now accepting applications for the cohort beginning in January 2025.

The application deadline is November 1, 2024

Program Description

The online Doctor of Engineering in Artificial Intelligence & Machine Learning is a research-based doctoral program. The program is designed to provide graduates with a solid understanding of the latest AI&ML techniques, as well as hands-on experience in applying these techniques to real-world problems. Graduates of this program are equipped to lead AI&ML projects and teams in a wide range of industries, including healthcare, finance, and manufacturing. Having developed advanced research skills, graduates are also well-prepared for academic research and teaching roles.

The degree requires completion of eight graduate-level courses (listed below) and a minimum of 24 credit hours of Praxis Research (SEAS 8588). During the research phase, the student writes and defends a research praxis on a topic related to AI&ML. The topic is selected by the student and approved by the research advising committee.

SEAS 6414 Python Application for Data Analytics:  Introduction to Python programming tailored for Data Analytics. This course covers Python’s applications in automating data cleaning, feature engineering, outlier detection, implementing machine learning algorithms, conducting text mining, and performing time series analysis. (3 credit hours)

SEAS 8500 Fundamentals of AI-Enabled Systems:  Operational decomposition for AI solutions, engineering data for algorithm development, and deployment strategies. Systems perspective in designing AI systems. Full-lifecycle of creating AI-enabled systems. Ethics and biases in AI systems (3 credit hours)

SEAS 8505 Applied Machine Intelligence and Reinforcement Learning:  Theory and practice of machine learning leveraging open-source tools, algorithms and techniques. Topics include intelligent model training, support vector machines, deep learning, transformer methods, GANs, and reinforcement learning (3 credit hours)

SEAS 8510 Analytical Methods for Machine Learning:  Mathematical tools for building machine learning algorithms: linear algebra, analytical geometry, matrix decompositions, optimization, probability and statistics (3 credit hours)

SEAS 8515 Data Engineering for AI:  Developing Python scripts to automate data pipelines, data ingestion, data processing, and data warehousing. Machine learning applications with Python including text mining and time series analysis (3 credit hours)

SEAS 8520 Deep Learning and Natural Language Processing:  Fundamentals of deep learning and Natural Language Processing (NLP). Techniques for designing modern deep learning networks using Keras and TensorFlow. NLP topics include sentiment analysis, bag of words, TFIDF, and Large Language Models (3 credit hours)

SEAS 8525 Computer Vision and Generative AI: Explore AI's visual realm. Learn image processing object detection, and models in generative adversarial networks and neural networks. Master tools for creating AI applications in art, design, ethical considerations, and societal impacts of generative AI technology (3 credit hours)

SEAS 8599 Praxis Development for AI & Machine Learning:  Overview of research methods. Aims and purpose of the praxis. Development of praxis research strategies, formulation, and defense of a praxis proposal (3 credit hours)

SEAS 8588 Praxis Research for D.Eng. in AI & Machine Learning:  Research leading to the degree of Doctor of Engineering in AI and Machine Learning (24 Credit Hours)  

Classroom courses last 10 weeks each and meet on Saturday mornings from 9:00 AM—12:10 PM and afternoons from 1:00—4:10 PM (all times Eastern). All classes meet live online through synchronous distance learning technologies (Zoom). All classes are recorded and available for viewing within two hours of the lecture. This program is taught in a cohort format in which students take all courses in lockstep. Courses cannot be taken out of sequence, live attendance at all class meetings is expected, and students must remain continuously enrolled. Leaves of absence are permitted only in the case of a medical or family emergency, or deployment to active military duty.  Please see below for the dates of our upcoming cohort.

No classes on Thanksgiving, Christmas, New Year, Fourth of July, and Memorial Day Weekends 

To proceed to the research phase, students must earn a grade point average of at least 3.2 in the eight classroom courses, and no grade below B-. Students are then registered for a minimum of 24 credit hours of SEAS 8588 Praxis Research: 3 ch in Fall 2025 (Session 2), 9 ch in Spring 2026, 3 ch in Summer 2026, and 9 ch in Fall 2026. Throughout the research phase, students develop the praxis under the guidance of a designated faculty advisor. Faculty research advisors are assigned by the program office and meet individually with students every two weeks.

Sample research areas are listed below:

•    Developing algorithms and methods that can explain how AI systems reach their decisions or predictions, making them more transparent and trustworthy •    Investigating how reinforcement learning can improve robotic performance and control, particularly in complex environments •    Examining how to ensure that AI systems are fair and unbiased in their decision-making, particularly in areas such as hiring, lending, and criminal justice •    Developing more advanced natural language processing models and algorithms that can understand and interpret human language more accurately and effectively •    Investigating how to apply transfer learning techniques to improve the performance of AI systems in new and different domains, with less data and less training time 

Tuition is $1,750 per credit hour for the 2024-2025 year and is billed at the beginning of each semester for the courses registered during that semester. A non-refundable tuition deposit of $995, which is applied to tuition due the first semester, is required when the applicant accepts the offer of admission.

Admissions Process

• Bachelor’s and master’s degrees in engineering, applied science, business, computer science, or a related field from accredited institutions.
• A minimum graduate-level GPA of 3.2
• Capacity for original scholarship.
• TOEFL, IELTS, Duolingo, or PTE scores are required of all applicants who are not citizens of countries where English is the official language.  Check our  International Students Page  to learn about the SEAS English language requirements and exemption policy. Test scores may not be more than two years old.

Note: GRE and GMAT scores are not required

Please note that our doctoral programs are highly selective; meeting minimum admissions requirements does not guarantee admission.  

• Attach up-to-date Resume 
• Attach Statement of Purpose – In an essay of 250 words or less, state your purpose in undertaking graduate study at The George Washington University. Describe your academic objectives, research interests, and career plans. Discuss your qualifications including collegiate, professional, and community activities, and any other substantial accomplishments not mentioned.
• Online Engineering Programs The George Washington University 170 Newport Center Drive Suite 260 Newport Beach, CA 92660

Normally, all transcripts must be received before an admission decision is rendered for the Doctor of Engineering program. 

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Fully Funded PhD Programs in Machine Learning

Last updated October 30, 2022

Next in my series on How to Fully Fund Your PhD , I provide a list below of universities that offer full funding for PhD Programs in Machine Learning. Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. A PhD in Machine Learning can provide pathways for careers in technology, research, and academia.

“Full funding” is a financial aid package for full-time students that includes full tuition and an annual stipend or salary for living expenses for the three to six-year duration of the student’s doctoral studies. Funding is typically offered in exchange for graduate teaching and research work that is complementary to your studies. Not all universities provide full funding to their doctoral students, which is why I recommend researching the financial aid offerings of all the potential Ph.D. programs in your academic field, including small and lesser-known schools both in the U.S. and abroad.

You can also find several external fellowships in the  ProFellow database for graduate and doctoral study, as well as dissertation research, fieldwork, language study, and summer work experience.

Would you like to receive the full list of more than 1000+ fully funded programs in 60 disciplines? Download the FREE Directory of Fully Funded Graduate Programs and Full Funding Awards !

Carnegie Mellon University, School of Computer Science

(Pittsburgh, PA): They committed to providing your full tuition and stipend support for the coming academic year as long as you continue to make satisfactory progress in our program. Students who do not have external financial support will be funded via graduate assistantships, awarded for a nine-month period. Also, provide a dependency allowance.

Johns Hopkins University, Department of Computer Science

(Baltimore, MD): All Computer Science, PhD students at JHU are guaranteed full funding for tuition, stipend, and health insurance, through a mix of research and teaching assistantships. The Department of Computer Science’s core research areas include theory and algorithms; security, privacy, and cryptography; computational biology and medicine; and machine learning and data-intensive computing.

University of Cambridge, PhD in Advanced Machine Learning

(Cambridge, UK & Tübingen, Germany): Available funding for up to two PhD Fellowships covering university tuition fees (at Cambridge EU rates) and a stipend of approximately 17,000 Euros.

University College London, PhD in Theoretical Neuroscience and Machine Learning

(London, United Kingdom): Students at the Gatsby Unit study toward a PhD in either machine learning or theoretical neuroscience. Gatsby Ph.D. studentships cover the cost of tuition at the appropriate rate and include a tax-free stipend of £26,000 per annum. Full funding is available to all students, regardless of nationality.

Stanford University, Department of Computer Science

(Stanford, CA): Most Computer Science PhD students are supported by a research or teaching assistantship in Computer Science or the School of Engineering (SOE), or by a fellowship, or by an approved assistantship through a collaborating research organization. The SOE’s PhD program is full-time and requires full tuition, most or all of which is normally covered by such support.

Harvard University, PhD in Computer Science includes Machine Learning

(Cambridge, Massachusetts): The financial aid program features guaranteed funding for the first five years to all Ph.D. students and a variety of funding options and fellowships for other students. This includes tuition, fees, and a cost-of-living stipend.

Looking for graduate funding? Sign up to discover and bookmark more than 2,400 professional and academic fellowships in the ProFellow database .

© Victoria Johnson 2020, all rights reserved.

Related Posts:

• Fully Funded PhD Programs in the United Kingdom
• 6 Artificial Intelligence Fellowships For All Career Levels
• Fully Funded PhD Programs in Neuroscience
• Fully Funded PhD Programs in Mathematics
• Fully Funded PhDs in Teaching English as a Second Language

Computer Science Fellowships , Fully Funded PhD Programs , Machine Learning Fellowships , PhD in Machine Learning

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Best Online Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salary

Machine learning is a rapidly growing, fascinating field dealing with algorithm development that can be used to make predictions from data. The best online PhD in Machine Learning prepares students for a career in this promising field.

The best online doctorates in machine learning offer students a comprehensive education in all aspects of the field. Students are also provided with the opportunity to choose a specialization such as deep learning, natural language processing , or computer vision. Find out in this article what machine learning PhD online degree program best fits you and the machine learning jobs for graduates.

Find your bootcamp match

Can you get a phd in machine learning online.

Yes, you can get a PhD in Machine Learning online. The online learning system has seen rapid growth in many academic fields and has given students the opportunity to virtually access the academic curriculum remotely.

Many online PhD programs in the United States are accredited and designed with working professionals in mind. Online learning is a great way to earn a doctorate without sacrificing your day job, and in most cases, online students can complete their entire academic journey without stepping foot on campus.

Is an Online PhD Respected?

Yes, an online PhD is respected when it is obtained from an accredited institution in the US. A PhD from an unaccredited school is regarded as just an expensive piece of paper by many other academic institutions.

In regard to employment, many companies and organizations respect an online PhD, holding it to the same standard as an in-person PhD. However, some employers prefer in-person degrees and will disregard online degrees. Ensure your potential future employer accepts online degrees as credible education.

What Is the Best Online PhD Program in Machine Learning?

The best online PhD program in machine learning is at Clarkson University in Potsdam, New York. It is regionally accredited by the Middle States Commission on Higher Education and has an excellent reputation within the academic community, a student-to-faculty ratio of 12 to one, and one in five of its 44,000 alumni is a CEO or executive.

Why Clarkson University Has the Best Online PhD Program in Machine Learning

Clarkson University has the best machine learning PhD program not only because it is accredited by the Middle States Commission on Higher Education (MSCHE) but also because of its US News & World Report ranking. MSCHE is a regionally recognized accreditation association that uses a rigorous and comprehensive system for the purpose of accreditation.

Referring to US News & World Report, Clarkson University is ranked 127 for best national universities out of 4000 degree-granting academic institutions in the United States and 49 for best value schools.

Best Online Master’s Degrees

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Online PhD in Machine Learning Admission Requirements

The admission requirements for an online PhD in Machine Learning typically include a master’s degree or Bachelor’s in Machine Learning or a related subject like the field of engineering. Moreover, prepare to submit official transcripts from previously attended postsecondary institutions and GRE test scores.

Additionally, you may be asked to submit three letters of recommendation, a statement of purpose, a CV or resume, and prove your knowledge of calculus and your fluency in computer programming languages like Python and Java. Below is a list of the typical admission requirements needed by distinct schools that offer a machine learning PhD program.

• Master’s or bachelor’s degree in a relevant field
• Official transcripts and GRE test scores
• Letters of recommendation
• Statement of purpose
• CV or resume
• Knowledge of programming and calculus

Best Online PhDs in Machine Learning: Top Degree Program Details

Best Online PhDs in Machine Learning: Top University Programs to Get a PhD in Machine Learning Online

The top university programs to get a PhD in Machine Learning are at Clarkson University, Aspen University, Capitol Technology University, The University of Rhode Island, and The University of the Cumberlands, among other distinct schools.

This section discusses the properties, requirements, and descriptions of the best universities offering online PhD in Machine Learning programs. We have created this list below to narrow down your school search for these graduate-level in-depth study programs.

Aspen University is a Distance Education Accrediting Commission accredited university. It was established in 1987 as a private for-profit online university offering undergraduate and graduate degrees in computer science, business information systems, and project management.

Aspen University in Phoenix, Arizona is a known member of the Council for Adult and Experiential Learning and is dedicated to supporting adult learners in achieving a professional career in whatever field they desire.

DSc in Computer Science

This doctoral degree teaches students the theory and practical application of computer science in data science, application design, and computer architecture. It contains 20 courses, including artificial intelligence, risk analysis, and system metrics. 

These courses are offered online and aim to impart students with the necessary skills for improving existing technology, as well as evaluating and applying them. It also contains courses that aid doctoral students in carrying out their research dissertations.

DSc in Computer Science Overview

• Accreditation: Distance Education Accrediting Commission
• Program Length: 5 years and 7 months
• Acceptance Rate: N/A
• Tuition and Fees: $375/month

DSc in Computer Science Admission Requirements

• Master’s degree
• Statement of goals
• Minimum of 3.0 GPA
• Must know about object-oriented development

Capitol Technology University was founded in 1927 and offers online programs at the undergraduate, graduate, and doctoral levels. The areas of study in which these online programs are offered include business, technology, and the field of engineering.

PhD in Artificial Intelligence

This is a research-based PhD program that offers students the opportunity to conduct research in any field of their choice. Throughout the program, student work must be approved by the academic supervisor. Students are to submit a thesis and give an oral presentation which will be supervised by an expert in the field.

PhD in Artificial Intelligence Overview

• Accreditation: Middle States Commission on Higher Education
• Program Length: 2 to 3 years
• Tuition and Fees: $933/credit

PhD in Artificial Intelligence Admission Requirements

• Application fee of $100
• Master’s degree in a relevant field
• Minimum of five years of related work experience
• Two recommendation letters

Founded in 1973, City University of Seattle is recognized as a top 10 educator of adults nationwide, as ranked by the US News & World Report for school rankings. It offers online undergraduate, graduate, and doctoral programs designed for working professionals

PhD in Information Technology

The program’s curriculum consists of courses in machine and deep learning. Candidates are given the option to propose their depth of study, which requires approval from the academic director. The program consists of core courses, concentration courses, a comprehensive examination, a research core, and a dissertation. 

PhD in Information Technology Overview

• Accreditation: Northwest Commission on Colleges and Universities
• Program Length: Flexible
• Acceptance Rate: 100% due to open admission policy
• Tuition and Fees: $765/credit

PhD in Information Technology Admission Requirements

• A master’s degree from an accredited or recognized institution
• CV and resume, and three references letters 
• Proof of English proficiency
• Interview with admissions advisor
• State goals related to your academic work

Founded in 1896 to honor Thomas S. Clarkson, Clarkson University offers flexible online degree programs at the undergraduate and graduate levels. It is a research university that leads in technology education. 

PhD in Computer Science

This doctoral program places emphasis on areas such as artificial intelligence , software, security, and networking. Current students are required to complete 36 credits of computer science foundation and research-oriented courses, elective courses, achieve candidacy within the first two years of the program, and propose and defend a thesis.

PhD in Computer Science Overview

• Program Length: 3 years
• Tuition and Fees: $1,533/credit

PhD in Computer Science Admission Requirements

• Complete the online application form
• Resume, statement of purpose, and three letters of recommendation
• English proficiency test for international applicants (TOEFL, IELTS, PTE, and Duolingo English Test)

Northcentral University is a private university established in 1996 and is designed for flexibility by offering programs of distance learning for working professionals. It practices a distinctive one-to-one learning system and has a dedicated doctoral faculty.

In this doctorate program, besides writing papers about past research, students are allowed to propose their research. Its curriculum consists of subjects such as software engineering , artificial intelligence, data mining, and cyber security. Through the course, students conduct research and examine real-world issues in the field of computer science.

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• Accreditation: WASC Senior College and University Commission
• Program Length: 3 years and 4 months
• Tuition and Fees: $1,094/credit
• Master’s degree from an accredited institution
• Official transcripts
• English proficiency exam score for international students

Nova Southeastern University was founded in 1964 in Fort Lauderdale, Florida. It offers online graduate and undergraduate courses and conducts a wide variety of interdisciplinary healthcare research. It is home to national athletics champions and Olympians.

This program provides research in computer science. Its format of learning combines both traditional and online instruction designed with consideration for working professionals . Its coursework consists of research in computer science areas, including cyber security, software engineering, and artificial intelligence.

• Accreditation: Southern Association of Colleges and Schools, Commission on Colleges
• Program Length: Not specified
• Tuition and Fees: $1,282/credit
• Online application and $50 application fee
• A bachelor’s or master’s degree in a relevant field from a regionally accredited institution
• GPA of at least 3.20 
• Official transcripts from all institutions attended 
• A resume  
• Essay, and three letters of recommendation

The University of North Dakota was founded in 1883, six years before North Dakota was made a state. Today, it offers several academic programs in undergraduate, graduate, and doctoral fields and is known for conducting research in areas that include medicine, aerospace, and engineering.

This PhD in Computer Science curriculum consists of courses in machine learning, software engineering, applications of AI, computer forensics, and computer networks which benefit students by granting them proficiencies in areas such as artificial intelligence, compiler design, operating systems, simulation, databases, and networks.

• Accreditation: Higher Learning Commission
• Program Length: 4 to 5 years
• Tuition and Fees: $545.16/credit (in state); $817.73/ credit (out of state)
• Application fee of $35
• Master’s or bachelor’s degree in engineering or a related science field
• GPA of 3.0 on a 4.0 scale and GRE test score
• Official copy of all college and university academic transcripts
• Statement of academic goals and three letters of recommendation
• Expertise in a high-level programming language and basic knowledge of data structures, formal languages, computer architecture and OS, calculus, statistics, and linear algebra 
• English language proficiency

The University of Rhode Island is a public research institution founded in 1892. It conducts extensive research in the field of science. It offers online, on-site, and hybrid programs at the graduate and undergraduate levels, as well as certificate programs.

In this PhD in Computer Science program, students are involved in research geared toward producing new intellectual and innovative contributions to the field of computer science. It offers a combination of on-campus, online, and day and evening classes. It consists of courses in machine learning, artificial intelligence, software engineering, and systems simulation.

• Accreditation: New England Commission of Higher Education
• Program Length: 4 years
• Tuition and Fees: $14,454/year (in-state); $27,906/ year (out of state)
• An undergraduate degree from a regionally accredited institution in the US
• A minimum GPA of 3.0
• All official college transcripts
• Personal statement
• An application fee of $65

Founded in 1888 by Baptist ministers in Williamsburg KY, today the University of the Cumberlands offers online master's and doctoral degree programs in the fields of education, information technology, and business.

The program requires 18 credit hours of core courses which include information technology geared toward creating machine learning engineers . Its curriculum focuses on predictive analytics and other skills students need to become experts in cyber crime security, big data, and smart technologies.

Students have the option to specialize in information systems security, information technology, digital forensics, or blockchain technologies. Students will complete 21 credit hours of professional research while working toward a dissertation.

• Tuition and Fees: $500/credit
• A master’s degree from a regionally accredited institution
• TOEFL for non-native English speakers
• Application fee of $30

Wright State University was first seen in 1964 as a branch campus for Ohio State University and Miami University. It is a Carnegie classified research university and offers research at the undergraduate, graduate, and doctoral levels.

PhD in Computer Science and Engineering

This degree is awarded to students who show excellence in study and research that significantly contributes to the field of computer science and engineering. The degree requirements include an A grade completion of the core coursework in two areas and at least a B in the third. 

Students are to complete a minimum of 18 hours of residency research before taking the candidacy exam, which must be completed with a satisfactory grade. Also, a minimum of 12 hours of dissertation research is needed before the dissertation defense, which has to be approved.

PhD in Computer Science and Engineering Overview

• Program Length: 10 years time limit
• Tuition and Fees: $660/credit (in state); $1,125/ credit (out of state)
• Bachelor’s or master’s degree in a related discipline (computer science or engineering)
• Minimum GPA of 3.0 if admitted with a bachelor’s degree or 3.3 with a master’s degree
• GRE general test portion
• TOEFL score for non-native English speakers
• Knowledge of high-level programming languages, computer organization, operating systems, data structures, and computer systems design
• A record that indicates potential for a career in research

Online Machine Learning PhD Graduation Rates: How Hard Is It to Complete an Online PhD Program in Machine Learning?

It is very hard to complete an online PhD in Machine Learning. According to a paper published in the International Journal of Doctoral Studies, there is a PhD attrition rate of 50 percent in the US within the past 50 years. Therefore, the graduation rate for doctorate students is approximately 50 percent.

How Long Does It Take to Get a PhD in Machine Learning Online?

It takes about four years to get a PhD in Machine Learning online, which is fast when compared to a traditional in-person PhD program which may take over seven years to complete. Online PhD programs are accelerated by default, so the curriculum focuses on the major needs of a PhD graduate in the areas of research, thesis, and dissertation.

Students may be able to reduce the time spent pursuing a PhD in Machine Learning by first acquiring a master’s degree in the field. If you choose to pursue a PhD on a part-time schedule as opposed to full-time study, it will significantly increase the time it takes to acquire the degree.

How Hard Is an Online Doctorate in Machine Learning?

Getting an online doctorate in machine learning is very hard, as are most graduate programs. Besides the rigorous research, strict requirements, deadlines, qualification examinations, and dissertations, other challenges may exist, such as limited student connection with the faculty members, isolation, financial issues, and lack of an adequate work-life balance .

Getting a doctorate in any field is not easy. In fact, there is research to suggest that online doctorate students face challenges regarding culture and academia. As a result of these challenges, many students drop out from their PhD programs.

Best PhD Programs

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What Courses Are in an Online Machine Learning PhD Program?

The courses in an online machine learning PhD program include an introduction to machine learning and deep learning, artificial intelligence, statistical theories, data mining , system simulation, computer programming, and software development.

Main Areas of Study in a Machine Learning PhD Program

• Machine learning
• Deep learning
• Artificial intelligence
• Databases and data mining
• Statistical theory
• Software engineering
• Systems simulation

How Much Does Getting an Online Machine Learning PhD Cost?

On average, it costs $19,314 per year to get a PhD in Machine Learning, according to the National Center of Education Statistics (NCES). However, this figure is not fixed, as the total tuition for a PhD program varies from school to school.

Private institutions generally cost more than public institutions, but there are funding opportunities for PhD students. Some PhD programs may guarantee financial aid for all their students regardless of merit.

How to Pay for an Online PhD Program in Machine Learning

You can pay for an online PhD in Machine Learning by taking advantage of student loans, scholarships, grants, teaching and research assistantships, graduate assistantships, and fellowship assistantships. As a result, most PhD students spend less than the tuition fee displayed on a school’s website.

How to Get an Online PhD for Free

You cannot get an online PhD in Machine Learning for free. However, there are ways to reduce the cost, or get partial tuition discounts and stipends through graduate assistantships, fellowships, scholarships, or grants.

What Is the Most Affordable Online PhD in Machine Learning Degree Program?

The most affordable online PhD in Machine Learning based on cost per credit is at Aspen University in Phoenix, Arizona. It charges $375 per month, which, when multiplied by the 67 months it takes to complete the program, results in a total of $25,125 for the entire program. This is more affordable compared to a school like Clarkson University, which charges $1,533 per credit hour.

Most Affordable Online PhD Programs in Machine Learning: In Brief

Why You Should Get an Online PhD in Machine Learning

You should get an online PhD in Machine Learning because having a PhD offers you a stronger advantage in terms of employability, salary, and in your career in general that would otherwise be unavailable with just a bachelor’s and master’s degree.

Top Reasons for Getting a PhD in Machine Learning

• Research opportunities. PhD students get the opportunity to be involved in rigorous and innovative research that may positively impact humanity, add to the world’s knowledge, and improve the lives of others.
• Expertise development. A PhD is the highest level of academic degree, and as a result, PhD holders have expert-level knowledge in whichever field they acquire a PhD in. However, it is advised to only get a PhD if you are very interested in the field and willing to explore your interest and expand your understanding through cutting-edge research.
• Access to better jobs. There are lots of bachelor’s and master’s degree graduates in the job market, and earning a PhD will help you stick out from the crowd. A PhD reveals career opportunities that may not be available to bachelor’s and master’s degree grads.
• Networking opportunities . During a PhD program, students are in contact with top lecturers and academic experts by attending guest lectures, conferences, seminars, and workshops. Students can network with colleagues and classmates, which helps put them in a good position after their academic journey.

Best Master’s Degree Programs

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What Is the Difference Between an On-Campus Machine Learning PhD and an Online PhD in Machine Learning?

The difference between an on-campus machine learning PhD and an online PhD in Machine Learning is primarily the mode of learning. Online PhDs are as rigorous and effective as their on-campus counterparts.

However, there may be some slight differences between the two in terms of cost, schedule, quality, and funding. Some of the differences that may exist are discussed below.

Online PhD vs On-Campus PhD: Key Differences

• Affordability. An online PhD is more affordable compared to the traditional on-campus alternative. An on-campus PhD can cost as much as $30,000 per year, while an online PhD may be as low as $20,000 per year.
• Flexibility. Online PhD students have the liberty to conduct in-depth study and research at their own time as opposed to the schedule of an in-person PhD program. Moreover, most online PhD programs don’t have an enrollment date, and some online PhD work is asynchronous, meaning students can take classes from anywhere at their convenience.
• Quality. Traditionally acquired PhDs are thought to be superior to their online counterparts by some employers and academics, probably due to sentiment. However, the quality of an online PhD is dependent on the research subject, the school’s reputation, and accreditation.
• Availability of funding. Funding available for online PhD programs may be limited due to some geographical constraints. For example, online PhD students cannot take up teaching assistantship positions unless they are willing to be physically present.

How to Get a PhD in Machine Learning Online: A Step-by-Step Guide

To get a PhD in Machine Learning, you need to first apply online to a PhD program. If accepted, you must enroll in the required classes and complete the academic coursework, research, and a series of academic milestones, which include attaining candidacy, passing the qualification examinations, proposing, writing, and defending your dissertation.

To begin your journey to acquiring a PhD in Machine Learning, you first need to apply online to the school of your choice. You also need to fulfill the admission requirements, including possessing a master's or bachelor's degree–depending on the school–in a relevant field, a minimum grade point average, letters of recommendation, and GRE test scores . 

Many online PhD programs require students to take and pass a minimum number of credit hours in core and elective courses. A typical online PhD in Machine Learning program consists of about 70 to 90 credit hours that involve intensive research in a provided or chosen area of concentration. 

Obtaining a PhD in Machine Learning allows an individual to become a world-renowned expert in the field. After completing a rigorous course of study and passing a series of exams, the doctoral candidate would then undertake an original research project that contributes new knowledge to the field. Upon successful completion of the degree, the graduate would be able to pursue a career in academia or industry. 

Examinations are an essential part of any education. They test a student's understanding of the material and help them to learn and remember the information. If you want to earn a machine learning PhD, you must pass the examinations for various core and required courses. Then, you will need to complete and defend your dissertation.

A dissertation is a research paper that is submitted to and defended by a graduate student to earn a graduate degree. To graduate with a PhD in Machine Learning, you are required to write a dissertation on a topic related to machine learning. Your doctoral dissertation must demonstrate your knowledge and understanding of the field of machine learning, as well as your ability to conduct original research in the field.

Online PhD in Machine Learning Salary and Job Outlook

The job outlook for machine learning jobs is 22 percent between 2020 and 2030 , with the number of new jobs expected in this time frame being 7,200, according to the US Bureau of Labor Statistics. The average salary for computer and information research scientists, which is a category that machine learning professionals belong to, is $131,490 per year .

What Can You Do With an Online Doctorate in Machine Learning?

With an online doctorate in machine learning, you can qualify for specialization roles and lead machine learning positions, including senior machine learning engineer and computer research scientist.

Depending on your preferences, you may also opt for a research and academic career path to become a university professor. The list below is a list of the best jobs for PhD in Machine Learning graduates.

Best Jobs with a PhD in Machine Learning

• Senior Machine Learning Engineer
• Computer and Information Research Scientist
• Data Scientist
• Software Engineer
• Postsecondary Teacher

Potential Careers With a Machine Learning Degree

[query_class_embed] how-to-become-a-*profession

What Is the Average Salary for an Online PhD Holder in Machine Learning? 

The average salary for a PhD in Machine Learning holder is $108,000 per year , according to PayScale’s salary for skills in machine learning. The average salary a PhD holder receives depends on the location and position you apply for.

Highest-Paying Machine Learning Jobs for PhD Grads

Best Machine Learning Jobs for Online PhD Holders

The best machine learning jobs for online PhD holders are typically high-paying jobs that require advanced-level skills that coincide with the nature of the position they undertake. Below are some typical job titles that online machine learning PhD degree holders assume.

A senior machine learning engineer oversees a team of machine engineers charged with designing and developing effective machine learning and deep learning solutions implemented in machine learning systems.

• Salary with a Machine Learning PhD: $153,255
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas

Computer and information research scientists research and develop new ways of solving complex computing problems and apply existing technology. They work to significantly increase the knowledge in the field of computer science, which will aid in the production of more efficient software and hardware technologies.

• Salary with a Machine Learning PhD: $131,490

A senior data scientist is responsible for developing data mining and machine learning techniques to solve complex business problems. They identify patterns and trends in large data sets, develop models to improve forecasting and decision making, and effectively communicate data-driven insights to non-technical stakeholders and lead a team of data analysts.

• Salary with a Machine Learning PhD: $127,455

A software engineer is a professional that develops and maintains software. They work on a variety of software, from operating systems to video games, and may be involved in the development of websites. They must also have an excellent understanding of computer programming languages and be able to solve complex problems.

• Salary with a Machine Learning PhD: $121,115
• Number of Jobs: 1,847,900
• Highest-Paying States: Washington, California, New York

Postsecondary teachers are in charge of lecturing students in colleges and universities. They are also responsible for instructing adults in several academic and non-academic subjects including career, work, and research.

• Salary with a Machine Learning PhD: $79,640
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900
• Highest-Paying States: California, Oregon, District of Columbia

Is It Worth It to Do a PhD in Machine Learning Online?

Yes, it is worth it to do a PhD in Machine Learning online. Getting a PhD is not for everyone, as the process will require tremendous effort and discipline, but it can be rewarding. A PhD in Machine Learning online allows you to learn from some of the best minds in the field.

You can also specialize in an area of your choice, such as big data, natural language processing, or deep learning. Specializing in one area for your PhD in Machine Learning allows you to deep-dive into that subject and build doctorate-level expertise.

An online PhD in Machine Learning provides students with the same high-quality education as a traditional PhD but with more flexibility and affordability. You’ll have access to top-notch instructors, state-of-the-art technology, and a thriving online community of experts.

Additional Reading About Machine Learning

[query_class_embed] https://careerkarma.com/blog/machine-learning/ https://careerkarma.com/blog/best-machine-learning-bachelors-degrees/ https://careerkarma.com/blog/best-machine-learning-masters-degrees/

Online PhD in Machine Learning FAQ

Yes, you should get an online PhD in Machine Learning if it is critical for your career prospects. An online PhD in Machine Learning allows you to learn at your own pace and keep your day job while you pursue your degree. In the end, it sets you up for the highest-earning jobs in the machine learning industry , with better pay and a larger professional network.

The type of research you will carry out as a machine learning student includes research in deep learning, neural networks , machine learning algorithms, supervised and unsupervised machine learning, predictive learning, and computer vision. Students will make use of quantitative and experimental methods of research as well as the use of optimal feature selection.

You can choose a concentration for an online machine learning PhD by factoring in your interests, strengths, and career goals. You may also consider recent trends, the average salary of machine learning professionals , or the career options the machine learning industry has to offer when choosing a machine learning concentration.

Examples of online machine learning PhD dissertations include experimental quantum speed-up in reinforcement learning agents, improving automated medical diagnosis systems with machine learning technologies, regulating deep learning and robotics, and the use of machines and robotics in medical procedures.

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Doctor of Philosophy (PhD) in Machine Learning

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Earn a Ph.D. in Machine Learning and discover the elements of artificial intelligence, computer engineering, and data analytics involved in this evolving field.

The doctoral degree in Machine Learning explores the ways in which algorithmic data is generated and leveraged for statistical applications and computational analysis in model-based decision-making. Students will learn the current operations, international relationships, and areas of improvement in this field, as well as research methodologies and future demands of the industry.

The PhD in Machine Learning is for current or experienced professionals in a field related to machine learning, artificial intelligence, computer science, or data analytics. Students will pursue a deep proficiency in this area using interdisciplinary methodologies, cutting-edge courses, and dynamic faculty. Graduates will contribute significantly to the Machine Learning field through the creation of new knowledge and ideas, and will quickly develop the skills to engage in leadership, research, and publishing. 

As your PhD progresses, you will move through a series of progression points and review stages by your academic supervisor. This ensures that you are engaged in research that will lead to the production of a high-quality thesis and/or publications, and that you are on track to complete this in the time available. Following submission of your PhD Thesis or accepted three academic journal articles, you will have an oral presentation assessed by an external expert in your field.

Why Capitol?

Expert guidance in doctoral research

Capitol’s doctoral programs are supervised by faculty with extensive experience in chairing doctoral dissertations and mentoring students as they launch their academic careers. You’ll receive the guidance you need to successfully complete your doctoral research project and build credentials in the field.

Proven academic excellence

Study at a university that specializes in industry-focused education in technology-based fields, nationally recognized for academic excellence in our programs.

Program is 100% online

Our PhD in Machine Learning is offered 100% online, with no on-campus classes or residencies required, allowing you the flexibility needed to balance your studies and career.

Key Faculty

Vice President

Associate Chair, Director of Engineering Labs

Dissertation Chair

Career Opportunities

Market demand for machine learning expertise

Graduates will contribute significantly to the rapidly growing machine learning field through the creation of new knowledge and ideas, and will be prepared for in-demand roles such as a trusted subject matter expert, researcher, technician, manager, or professor.

This program may be completed with a minimum of 60 credit hours, but may require additional credit hours, depending on the time required to complete the dissertation/publication research. Students who are not prepared to defend after completion of the 60 credits will be required to enroll in RSC-899, a one-credit, eight-week continuation course. Students are required to be continuously enrolled/registered in the RSC-899 course until they successfully complete their dissertation defense/exegesis.

The student will produce, present, and defend a doctoral dissertation after receiving the required approvals from the student’s Committee and the PhD Review Boards.

Doctor of Philosophy in Machine Learning Courses Total Credits: 60

MACHINE LEARNING DOCTORAL CORE: 30 CREDITS

OFFENSIVE MACHINE LEARNING DOCTORAL RESEARCH AND WRITING: 30 CREDITS 

Educational Objectives:  

Students will... 

1. Integrate and synthesize alternate, divergent, or contradictory perspectives within the field of Machine Learning. 2. Demonstrate advanced knowledge and competencies in ethics of Machine Learning. 3. Analyze theories, tools, and frameworks used in Machine Learning. 4. Evaluate the legal, social, economic, environmental, and ethical impact of actions within Machine Learning. 5. Implement Machine Learning plans needed for advanced global applications.

Learning Outcomes:  

Upon graduation... 

1. Graduates will integrate the theoretical basis and practical applications of Machine Learning into their professional work.  2. Graduates will demonstrate the highest mastery of the subject matter. 3. Graduates will evaluate complex problems, synthesize divergent/alternative/contradictory perspectives and ideas fully, and develop advanced solutions to Machine Learning challenges. 4. Graduates will contribute to the body of knowledge in the study of the subject. 5. Graduates will be at the forefront of Machine Learning planning and implementation.

Tuition & Fees

Tuition rates are subject to change.

The following rates are in effect for the 2024-2025 academic year, beginning in Fall 2024 and continuing through Summer 2025:

• The application fee is $100
• The per-credit charge for doctorate courses is $950. This is the same for in-state and out-of-state students.
• Retired military receive a $50 per credit hour tuition discount
• Active duty military receive a $100 per credit hour tuition discount for doctorate level coursework.
• Information technology fee $40 per credit hour.
• High School and Community College full-time faculty and full-time staff receive a 20% discount on tuition for doctoral programs.

Find additional information for 2024-2025 doctorate tuition and fees.

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• Contest leads to 5 new models of wearable sensors for freezing of gait

Winners of global machine-learning contest in Parkinson's split $100K prize

by Steve Bryson, PhD | August 23, 2024

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Five top models for a wearable sensor that can monitor and measure freezing of gait (FOG) — a sudden inability to walk — in people with Parkinson’s disease were developed as the result of a three-month, international machine-learning contest launched by scientists at Tel Aviv University in Israel.

The teams behind these winning models, which the scientists say potentially “could replace or augment visual, time-consuming video [analysis] by expert reviewers” — the current gold standard for FOG assessment — split a $100,000 prize.

“This successful endeavor underscores the potential of machine learning contests to rapidly engage AI [artificial intelligence] experts in addressing critical medical challenges and provides a promising means for objective FOG quantification,” the scientists wrote, adding that “the winning efforts markedly improved previous [FOG] detection abilities.”

According to Jeffrey M. Hausdorff, PhD, the study lead at the university’s faculty of medical & health sciences and the Sagol School of Neuroscience, the models were based on machine learning, in which computer programs learn from inputted data.

“Wearable sensors supported by machine learning models can continuously monitor and quantify FOG episodes, as well as the patient’s general functioning in daily life. This gives the clinician an accurate picture of the patient’s condition at all times,” Hausdorff said in a university press release , noting using such devices would allow healthcare providers to see if patients respond to prescribed medications.

“The informed clinician can respond promptly, while data collected through this technology can support the development of new treatments,” Hausdorff said.

The contest and its outcomes were detailed in a study titled “ A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects ,” published in Nature Communications.

Freezing of gait more likely to occur with long-term levodopa use

Fog detection challenge launched for machine learning community.

Gait refers to a person’s manner of walking. Freezing of gait, or an inability to lift the foot and take a step — known as FOG for short — is a disabling symptom that affects up to 65% of people with Parkinson’s.

“A FOG episode can last from a few seconds to more than a minute, during which the patient’s feet are suddenly ‘glued’ to the floor, and the person is unable to begin or continue walking,” said Hausdorff, who’s also at the Center for the Study of Movement, Cognition and Mobility at the Tel Aviv Medical Center.

“FOG can seriously impair the mobility, independence, and quality of life of people with Parkinson’s disease, causing great frustration, and frequently leading to falls and injuries,” Hausdorff noted.

The gold standard for assessing FOG, used in addition to self-report questionnaires and visual observation by clinicians, is a frame-by-frame video analysis of patients showing them moving. While accurate, such video assessment has limitations, such as the need for multiple specialists. It’s also time-consuming. Moreover, long-term video monitoring inside the home is impractical.

Although there is a growing effort to develop wearable sensors to track and measure a patient’s daily functioning, “successful trials have all relied on a very small number of subjects,” Hausdorff said.

Such constraints led Hausdorff and colleagues to launch a FOG detection challenge for the machine learning community. The goal was to accelerate the development of a reliable, cost-effective, and automatic detection method for freezing of gait.

A prize of $100,000, divided among the top five finishers, was funded by the Michael J. Fox Foundation for Parkinson’s Research and Kaggle , a Google company that conducts international machine learning competitions. Altogether, there were 24,862 submissions from 1,379 teams across 83 countries.

The participants created machine-learning models based on data collected from Parkinson’s patients wearing a single lower-back sensor and from video analysis, which recorded about 5,000 FOG episodes in total. The contest’s parameters were to detect and classify three types of freezing of gate events: start hesitation, FOG during turns, and FOG episodes during walking.

Wearable Leg Sensors Accurately Detect Parkinson’s Freezing of Gait

Freezing of gait most likely to occur at about 7 a.m. and 10 p.m..

Submissions were ranked based on performance against two data sets with 40 Parkinson’s patients and more than 1,300 validated FOG episodes. All five of the top models showed good accuracy in detecting all FOG classes, ranging from 88% to 92%.

The best performance was for FOG during turns, the most common FOG class. Still, there was a trade-off across the models between successfully detecting walking events and start hesitation. Freezing of gait episodes during walking were the hardest to detect, while start hesitation performance was generally better. Ultimately, the top model best balanced these two classes.

A correlation analysis then compared the model estimates with the actual percent time frozen (%TF), the number of freezing episodes, and the duration of each episode per patient, as assessed by video.

All five models show good to excellent statistically significant %TF correlations, “indicating that the models accurately estimated the percentage of the total time spent freezing,” the researchers wrote. The correlation for the total FOG duration also was excellent, but was weaker for the number of freezing episodes.

The contest we initiated brought together capable, dynamic teams all over the world, who enjoyed a friendly atmosphere of learning and competition for a good cause. … Rapid improvement was gained in the effective and precise quantification of FOG data.

Finally, the team estimated the %TF over time between Parkinson’s patients with and without FOG, as assessed during a clinical visit or the patient-reported new freezing of gait questionnaire.

In the FOG group, there was a significant difference between the hourly %TF during the daytime and the median %TF at night. Moreover, there appeared to be two peaks of freezing episodes — at about 7 a.m. and 10 p.m.

“We observed, for the first time, a recurring daily pattern, with peaks of FOG episodes at certain hours of the day, that may be associated with clinical phenomena such as fatigue, or effects of medications,” said Eran Gazit, a co-author of the study from the Tel Aviv Medical Center. “These findings are significant for both clinical treatment and continued research about FOG.”

Hausdorff noted that “rapid improvement was gained in the effective and precise quantification of FOG data.”

But for this scientist, there was another very important result: “Our study demonstrates the power of machine learning contests in advancing medical research,” Hausdorff said.

“The contest we initiated brought together capable, dynamic teams all over the world, who enjoyed a friendly atmosphere of learning and competition for a good cause, Hausdorff said. “Moreover, the study laid the foundations for the next stage: long-term 24/7 FOG monitoring in the patient’s home and real-world environment.”

About the Author

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Why data remains the greatest challenge for machine learning projects

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Quality data is at the heart of the success of enterprise artificial intelligence (AI). And accordingly, it remains the main source of challenges for companies that want to apply machine learning (ML) in their applications and operations.

The industry has made impressive advances in helping enterprises overcome the barriers to sourcing and preparing their data, according to Appen’s latest State of AI Report. But there is still a lot more to be done at different levels, including organization structure and company policies.

The costs of data

The enterprise AI life cycle can be divided into four stages: Data sourcing, data preparation, model testing and deployment, and model evaluation. 

Advances in computing and ML tools have helped automate and accelerate tasks such as training and testing different ML models. Cloud computing platforms make it possible to train and test dozens of different models of different sizes and structures simultaneously. But as machine learning models grow in number and size, they will require more training data.

Unfortunately, obtaining training data and annotating still requires considerable manual effort and is largely application specific. According to Appen’s report, “lack of sufficient data for a specific use case, new machine learning techniques that require greater volumes of data, or teams don’t have the right processes in place to easily and efficiently get the data they need.”

“High-quality training data is required for accurate model performance; and large, inclusive datasets are expensive,” Appen’s chief product officer Sujatha Sagiraju told VentureBeat. “However, it’s important to note that valuable AI data can increase the chances of your project going from pilot to production; so, the expense is needed.”

ML teams can start with prelabeled datasets, but they will eventually need to collect and label their own custom data to scale their efforts. Depending on the application, labeling can become extremely expensive and labor-intensive. 

In many cases, companies have enough data, but they can’t deal with quality issues. Biased, mislabeled, inconsistent or incomplete data reduces the quality of ML models, which in turn harms the ROI of AI initiatives. 

“If you train ML models with bad data, model predictions will be inaccurate,” Sagiraju said. “To ensure their AI works well in real-world scenarios, teams must have a mix of high-quality datasets, synthetic data and human-in-the-loop evaluation in their training kit.”

The gap between data scientists and business leaders

According to Appen, business leaders are much less likely than technical staff to consider data sourcing and preparation as the main challenges of their AI initiatives. “There are still gaps between technologists and business leaders when understanding the greatest bottlenecks in implementing data for the AI lifecycle. This results in misalignment in priorities and budget within the organization,” according to the Appen report.

“What we know is that some of the biggest bottlenecks for AI initiatives lie in lack of technical resources and executive buy-in,” Sagiraju said. “If you take a look at these categories, you see that the data scientists, machine learning engineers, software developers and executives are dispersed across different areas, so it’s not hard to imagine a lack of aligned strategy due to conflicting priorities between the various teams within the organization.”

The variety of people and roles involved in AI initiatives makes it hard to achieve this alignment. From the developers managing the data, to the data scientists dealing with on-the-ground issues, and the executives making strategic business decisions, all have different goals in mind and therefore different priorities and budgets. 

However, Sagiraju sees that the gap is slowly narrowing year over year when it comes to understanding the challenges of AI. And this is because organizations are better understanding the importance of high-quality data to the success of AI initiatives. 

“The emphasis on how important data — especially high-quality data that match with application scenarios — is to the success of an AI model has brought teams together to solve these challenges,” Sagiraju said.

Promising trends in machine learning

Data challenges are not new to the field of applied ML. But as ML models grow bigger and data becomes more abundantly available, there is a need to find scalable solutions to assemble quality training data.

Fortunately, a few trends are helping companies overcome some of these challenges, and Appen’s AI Report shows that the average time spent in managing and preparing data is trending down.

One example is automated labeling. For example, object detection models require the bounding boxes of each object in the training examples to be specified, which takes considerable manual effort. Automated and semi-automated labeling tools use a deep learning model to process the training examples and predict the bounding boxes. The automated labels are not perfect, and a human labeler must review and adjust them, but they speed up the process significantly. In addition, the automated labeling system can be further trained and improved as it receives feedback from human labelers.

“While many teams start off with manually labeling their datasets, more are turning to time-saving methods to partially automate the process,” Sagiraju said.

At the same time, there is a growing market for synthetic data . Companies use artificially generated data to complement the data they collect from the real world. Synthetic data is especially useful in applications where obtaining real-world data is costly or dangerous. An example is self-driving car companies, which face regulatory, safety and legal challenges in obtaining data from real roads.

“Self-driving cars require incredible amounts of data to be safe and prepared for anything once they hit the road, but some of the more complex data is not readily available,” Sagiraju said. “Synthetic data allows practitioners to account for edge cases or dangerous scenarios like accidents, crossing pedestrians and emergency vehicles to effectively train their AI models. Synthetic data can create instances to train data when there isn’t enough human-sourced data. It’s critical in filling in the gaps.”

At the same time, the evolution of the MLops market is helping companies tackle many challenges of the machine learning pipeline, including labeling and versioning datasets; training, testing, and comparing different ML models; deploying models at scale and keeping track of their performance; and gathering fresh data and updating the models over time. 

But as ML plays a greater role in enterprises, one thing that will become more important is human control. 

“Human-in-the-loop (HITL) evaluations are imperative to delivering accurate, relevant information and avoiding bias,” Sagiraju said. “Despite what many believe about humans actually taking a backseat in AI training, I think we’ll see a trend towards more HITL evaluations in an effort to empower responsible AI, and have more transparency about what organizations are putting into their models to ensure models perform well in the real world.”

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Machine Learning - CMU

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

• All categories

PhD candidate, Feature-based machine learning for precision diagnosis of neuromuscular diseases

The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a:

Neuromuscular disorders, which affect millions of people in Europe alone, lead to (progressive) muscle weakness or sensory deficits that gravely affect life expectancy and quality of life. To diagnose the disorders, needle electromyography (nEMG) data must be assessed audio-visually by experts, which is subjective and time-consuming.

In this project, experts in computer science and clinical neurophysiology will collaborate with a commercial partner to develop an artificial-intelligence platform integrating feature-based and deep learning approaches to automatically, objectively and accurately interpret nEMG data to improve the diagnosis of neuromuscular disorders ensuring explainability and responsible AI practices. Researchers of this project will validate the method using real nEMG data from around the world and take first steps towards integrating the platform into the existing software for clinical use. This position offers a unique opportunity to contribute to the cutting-edge research that will significantly impact the clinical landscape of neuromuscular disorders.

More specifically, the PhD researcher employed in this position will build on the earlier collaboration between LIACS and Leiden University Medical Center which has produced a smaller study yielding promising preliminary results using a feature-based automated classification pipeline. In the later stages of the project, the researcher will investigate the hybridisations of her/his approach with the deep learning techniques to improve the diagnostic yield.

Key responsibilities The person employed in this PhD position will be responsible for:

• conducting original and novel research in the field of applied machine learning;
• development of the methodology of classification of nEMG recordings complementary to the deep learning approach;
• design and implementation of the feature-based machine learning platform for the classification of nEMG recordings;
• integration of the developed platform within the project’s toolbox and multicentre database of anonymised patient data;
• collaboration with researchers in their own group, two other PhD students involved in the project in other institutions and their respective research groups;
• publishing and presenting scientific results at international conferences and journals;
• completing the courses on academic and transferable skills as required by Leiden University;
• providing assistance in relevant teaching activities within LIACS.

Selection Criteria The successful applicant should be a motivated university graduate who is a top performer among his/her peers and has an excellent education and/or research track record proven by relevant experience, publications, etc. Candidates in the final stages of obtaining their degree are eligible to apply. The applicant is expected to have or be close to obtaining:

• MSc degree in Computer science, Applied Mathematics, Physics, Artificial Intelligence, Data science or related field;
• Excellent programming skills in, e.g., Python and/or C++ (as evidenced by, e.g., a code repository link);
• Credible experience with Machine Learning and/or Data science projects;
• Excellent written and oral communication skills in English, Dutch proficiency or willingness to learn is a plus;
• Ability to work with diverse stakeholders, along with an affinity for connecting work in Computer Science to other relevant disciplines.

Research at our faculty The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. It is a faculty where personal and academic development are top priorities. Our people are committed to expand fundamental knowledge by curiosity and to look beyond the borders of their own discipline; their aim is to benefit science, and to contribute to addressing the major societal challenges of the future.

The research carried out at the Faculty of Science is very diverse, ranging from mathematics, information science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The research activities are organised in eight institutes. These institutes offer eight bachelor’s and twelve master’s programmes. The faculty has grown strongly in recent years and now has more than 2.300 staff and almost 5,000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together. For more information, see https://www.universiteitleiden.nl/en/science and https://www.universiteitleiden.nl/en/working-at

The Leiden Institute of Advanced Computer Science (LIACS) is the Artificial Intelligence and Computer Science Institute in the Faculty of Science of Leiden University. We offer courses at the Bachelor and Master of Science level in Artificial Intelligence, Computer Science, ICT in Business, Media Technology, and Bioinformatics. According to an independent research visitation, we are one of the foremost computer science departments of the Netherlands. We strive for excellence in a caring institute, where excellence, fun, and diversity go hand in hand. We offer a clear and inviting career path to young and talented scientists with the ambition to grow. For more information about LIACS, see https://www.cs.leiden.edu

Terms and conditions We offer a full-time position for one year initially. After a positive evaluation of the progress of the thesis, personal capabilities and compatibility, the appointment will be extended by further three years. Salary ranges from € 2.770,- to € 3.539,- gross per month (pay scale P in accordance with the Collective Labour Agreement for Dutch Universities). Preferred starting date for this position is November 1, 2024 or soon thereafter. Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3%), training and career development and sabbatical leave. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be eligible for a substantial tax break.

All our PhD students are embedded in the Leiden University Graduate School of Science https://www.universiteitleiden.nl/en/science/graduate-school-of-of-science Our graduate school offers several PhD training courses at three levels: professional courses, skills training and personal effectiveness. In addition, advanced courses to deepen scientific knowledge are offered by the research school.

Within this project, PhD students are encouraged to spend a semester abroad, and a budget is available to cover their expenses. Moreover, generous (conference) travel budgets are available for the position.

D&I statement Diversity and inclusion are core values of Leiden University. Leiden University is committed to becoming an inclusive community which enables all students and staff to feel valued and respected and to develop their full potential. Diversity in experiences and perspectives enriches our teaching and strengthens our research. High quality teaching and research is inclusive.

Information Enquiries on the technical content of this position can be made to Dr Anna Kononova, [email protected] . If you have any questions about the procedure, please send an email to [email protected] .

Applications Please submit online your application via the blue button in the vacancy. Applications submitted via email will not be considered.

Please ensure that you upload the following additional documents quoting the vacancy number:

• Motivation letter
• Curriculum vitae
• Academic transcript of the MSc degree
• Names of 2-3 references if applicable

Only applications received before September 22, 2024 can be considered. Selected candidates will be invited for an interview in the beginning of October 2024.

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. This post will dive deeper into the nuances of each field.

Data science is a broad, multidisciplinary field that extracts value from today’s massive data sets. It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming.

Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve. Data science solves a business problem by understanding the problem, knowing the data that’s required, and analyzing the data to help solve the real-world problem.

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. It requires data science tools to first clean, prepare and analyze unstructured big data. Machine learning can then “learn” from the data to create insights that improve performance or inform predictions.

Just as humans can learn through experience rather than merely following instructions, machines can learn by applying tools to data analysis. Machine learning works on a known problem with tools and techniques, creating algorithms that let a machine learn from data through experience and with minimal human intervention. It processes enormous amounts of data a human wouldn’t be able to work through in a lifetime and evolves as more data is processed.

Across most companies, finding, cleaning and preparing the proper data for analysis can take up to 80% of a data scientist’s day. While it can be tedious, it’s critical to get it right.

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored.

One challenge in applying data science is to identify pertinent business issues. For example, is the problem related to declining revenue or production bottlenecks? Are you looking for a pattern you suspect is there, but that’s hard to detect? Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics.

With the increase in data from social media, e-commerce sites, internet searches, customer surveys and elsewhere, a new field of study based on big data emerged. Those vast datasets, which continue to increase, let organizations monitor buying patterns and behaviors and make predictions.

Because the datasets are unstructured, though, it can be complicated and time-consuming to interpret the data for decision-making. That’s where data science comes in.

The term data science (link resides outside of ibm.com) was first used in the 1960s when it was interchangeable with the phrase “computer science.” “Data science” was first used as an independent discipline  (link resides outside of ibm.com) in 2001. Both data science and machine learning are used by data engineers and in almost every industry.

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know  Structured Query Language (SQL)  as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. It’s also necessary to understand data cleaning and processing techniques. Because data analysts often build machine learning models, programming and AI knowledge are also valuable. as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. It’s also necessary to understand data cleaning and processing techniques. Because data analysts often build machine learning models, programming and AI knowledge are also valuable.

Data science is widely used in industry and government, where it helps drive profits, innovate products and services, improve infrastructure and public systems and more.

Some examples of data science use cases include:

• An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app.
• A manufacturer developed powerful, 3D-printed sensors to guide driverless vehicles.
• A police department’s statistical incident analysis tool helps determine when and where to deploy officers for the most efficient crime prevention.
• An AI-based medical assessment platform analyzes medical records to determine a patient’s risk of stroke and predict treatment plan success rates.
• Healthcare companies are using data science for breast cancer prediction and other uses.
• One ride-hailing transportation company uses big data analytics to predict supply and demand, so they can have drivers at the most popular locations in real time. The company also uses data science in forecasting, global intelligence, mapping, pricing and other business decisions.
• An e-commerce conglomeration uses predictive analytics in its recommendation engine.
• An online hospitality company uses data science to ensure diversity in its hiring practices, improve search capabilities and determine host preferences, among other meaningful insights. The company made its data open-source, and trains and empowers employees to take advantage of data-driven insights.
• A major online media company uses data science to develop personalized content, enhance marketing through targeted ads and continuously update music streams, among other automation decisions.

The start of machine learning, and the name itself, came about in the 1950s. In 1950, data scientist Alan Turing proposed what we now call the Turing Test  (link resides outside of ibm.com), which asked the question, “Can machines think?” The test is whether a machine can engage in conversation without a human realizing it’s a machine. On a broader level, it asks if machines can demonstrate human intelligence. This led to the theory and development of AI.

IBM computer scientist Arthur Samuel (link resides outside of ibm.com) coined the phrase “machine learning” in 1952. He wrote a checkers-playing program that same year. In 1962, a checkers master played against the machine learning program on an IBM 7094 computer, and the computer won.

Today, machine learning has evolved to the point that engineers need to know applied mathematics, computer programming, statistical methods, probability concepts, data structure and other computer science fundamentals, and big data tools such as Hadoop and Hive. It’s unnecessary to know SQL, as programs are written in R, Java, SAS and other programming languages. Python is the most common programming language used in machine learning.

Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. It can recognize complex patterns in text, images, sounds, and other data and create accurate insights and predictions. Deep learning algorithms are neural networks modeled after the human brain.

Subcategories of machine learning

Some of the most commonly used machine learning algorithms  (link resides outside of ibm.com) include linear regression , logistic regression, decision tree , Support Vector Machine (SVM) algorithm, Naïve Bayes algorithm and KNN algorithm . These can be supervised learning, unsupervised learning or reinforced/reinforcement learning.

Machine learning engineers can specialize in natural language processing and computer vision, become software engineers focused on machine learning and more.

There are some ethical concerns regarding machine learning, such as privacy and how data is used. Unstructured data has been gathered from social media sites without the users’ knowledge or consent. Although license agreements might specify how that data can be used, many social media users don’t read that fine print.

Another problem is that we don’t always know how machine learning algorithms work and “make decisions.” One solution to that may be releasing machine learning programs as open-source, so that people can check source code.

Some machine-learning models have used datasets with biased data, which passes through to the machine-learning outcomes. Accountability in machine learning refers to how much a person can see and correct the algorithm and who is responsible if there are problems with the outcome.

Some people worry that AI and machine learning will eliminate jobs. While it may change the types of jobs that are available, machine learning is expected to create new and different positions. In many instances, it handles routine, repetitive work, freeing humans to move on to jobs requiring more creativity and having a higher impact.

Well-known companies using machine learning include social media platforms, which gather large amounts of data and then use a person’s previous behavior to forecast and predict their interests and desires. The platforms then use that information and predictive modeling to recommend relevant products, services or articles.

On-demand video subscription companies and their recommendation engines are another example of machine learning use, as is the rapid development of self-driving cars. Other companies using machine learning are tech companies, cloud computing platforms, athletic clothing and equipment companies, electric vehicle manufacturers, space aviation companies, and many others.

Practicing data science comes with challenges. There can be fragmented data, a short supply of data science skills, and tools, practices, and frameworks to choose between that have rigid IT standards for training and deployment. It can also be challenging to operationalize ML models that have unclear accuracy and predictions that are difficult to audit.

IBM’s data science and AI lifecycle product portfolio is built upon our longstanding commitment to open-source technologies. It includes a range of capabilities that enable enterprises to unlock the value of their data in new ways.

Watsonx  is a next generation data and AI platform built to help organizations multiply the power of AI for business. The platform comprises three powerful components: the  watsonx.ai  studio for new foundation models, generative AI and machine learning; the watsonx.data fit-for-purpose store for the flexibility of a data lake and the performance of a data warehouse; plus, the watsonx.governance toolkit, to enable AI workflows that are built with responsibility, transparency and explainability.

Together, watsonx offers organizations the ability to:

• Train, tune and deploy AI across your business with  watsonx.ai
• Scale AI workloads, for all your data, anywhere with  watsonx.data
• Enable responsible, transparent and explainable data and AI workflows with  watsonx.governance

Learn more about IBM watsonx