Courses on machine learning

This page gives a summary of courses related to various aspects of machine learning from 60+ universities worldwide. (In alphabetical order with respect to the names of the universities.)

As well as this course list, we have also:

• Collated statistics on the number hours per topic in the courses from 9 of the universities. (warning - this takes a little time to load)
• In progress: Proposed example syllabi for introductory and advanced courses of 10, 20 and 40 hours.

The courses are:

1. Index of Machine Learning Courses (Russell Greiner, Department of Computing Science, University of Alberta)
2. Index of Machine Learning Courses (Vasant Honavar and Oksana Yakhnenko, Artificial Intelligence Research Group, Department of Computer Science, Iowa State University)
3. Machine Learning Resources: Courses (David W. Aha)
4. Aalborg University, Denmark: Decision support systems and machine learning (Thomas D. Nielsen)
5. Aalborg University, Denmark: Machine Learning (Zheng-Hua Tan)
6. University of Alberta: Introduction to Machine Learning (Russ Greiner and Shaojun Wang)
7. University at Buffalo: Machine Learning (Sargur Srihari)
8. University of California, Berkeley: Practical Machine Learning (Prof Michael Jordan)
9. University of California, Berkeley: Statistical Learning Theory (Prof, Michael Jordan)
10. The University of Birmingham: Machine Learning (Dr A Kaban)
11. University of Bristol , UK Introduction to Machine Learning (Rafal Bogacz)
12. University of British Columbia: Algorithms for Classification and Prediction (Kevin Murphy)
13. University of British Columbia: Machine learning (Kevin Murphy)
14. University of British Columbia: Probabilistic graphical models (Kevin Murphy)
15. Brown University: Introduction to Machine Learning (Greg Shakhnarovich)
16. University of Cambridge: Machine Learning (Zoubin Ghahramani)
17. Carnegie Mellon University: Machine Learning (Carlos Guestrin)
18. Carnegie Mellon University: Machine Learning (Tom M. Mitchell & Andrew W. Moore)
19. Carnegie Mellon University: Statistical Machine Learning (John Lafferty and Larry Wasserman)
20. Cornell University: Advanced Topics in Machine Learning (Richard Caruana)
21. Cornell University: Empirical Methods in Machine Learning and Data Mining (Richard Caruana)
22. Cornell University: Machine Learning (Thorsten Joachims)
23. Cornell University: Topics in Machine Learning (Thorsten Joachims/Richard Caruana)
24. Cornell University: Topics in Machine Learning Learning to Predict Structured Objects (Thorsten Joachims)
25. University of Edinburgh: Data Mining and Exploration (Amos Storkey)
26. University of Edinburgh: Learning from Data (Amos Storkey)
27. University of Edinburgh: Machine Learning & Sensorimotor Control (Dr. Sethu Vijayakumar)
28. University of Edinburgh: Probabilistic Modelling and Reasoning (Prof. Chris Williams)
29. University of Edinburgh: Reinforcement learning (Gillian Hayes)
30. University of Essex, UK: Machine Learning and Data Mining (Paul Scott)
31. University of Freiburg, Germany: Logic, Language and Learning (Prof. Dr. Luc De Raedt)
32. University of Glasgow: Machine Learning Module (Professor. M. A .Girolami)
33. University of Houston: Machine Learning (Ricardo Vilalta)
34. IDIAP Research Institute, Switzerland: Statistical Machine Learning from Data (Samy Bengio)
35. Imperial College London: Machine Learning (Stephen Muggleton, Maja Pantic)
36. Iowa State University: Machine Learning (Vasant Honavar)
37. Johns Hopkins University: Machine Learning (Mark Dredze)
38. The University of Manchester: Machine Learning (Dr. Magnus Rattray and Dr. Aphrodite Galata)
39. Massachusetts Institute of Technology: Machine Learning (Tommi Jaakkola)
40. New York University: Machine Learning and Pattern Recognition (Yann LeCun)
41. University of Pennsylvania: Advanced Topics in AI - Probablistic Graphical Models (Ben Taskar and Koby Crammer)
42. University of Pennsylvania: Advanced Topics in Machine Learning 2004 (Prof. Michael Kearns)
43. University of Pennsylvania: Artificial Intelligence and Machine Learning (Prof. Lyle Ungar)
44. University of Pennsylvania: Machine Learning for Language Processing (Prof. Fernando Pereira)
45. University of Pennsylvania: Machine-Learning Models and Algorithms for Structured Data (Prof. Fernando Pereira)
46. University of Pennsylvania: Vision and Learning (Jianbo Shi)
47. University of Pittsburgh: Machine Learning (Milos Hauskrecht)
48. Princeton University: Graphical Models (David Blei)
49. Queen Mary University of London: Machine Learning
50. Royal Holloway University of London: Machine Learning Strand (A. Gammerman, V.N. Vapnik, V. Vovk, C. Watkins)
51. The University of Sheffield: Machine Learning Foundations (Professor Rob Gaizauskas)
52. Sofia University: Machine Learning (Zdravko Markov)
53. Stanford University: Machine Learning (Andrew Ng)
54. Technischen Universität Müchen: Machine Learning Lab Course (Professor Jürgen Schmidhuber)
55. The University of Texas: Machine Learning (Raymond J. Mooney)
56. University College London: Advanced Topics in Machine Learning (Massimiliano Pontil)
57. University College London: Advanced Topics in Machine Learning (Robert E Smith)
58. University College London: Unsupervised Learning (Zoubin Ghahramani and Maneesh Sahani)
59. The University of Utah: Machine Learning (Hal Daume III)
60. Växjö University, Sweden: Machine Learning (Joakim Nivre, Torbjörn Lager, Walter Daelemans, James Cussens)
61. The Weizmann Institute of Science, ISRAEL: Statistical Machine Learning (Jacob Goldberger)
62. University of Westminster: Machine Learning (Dr Dimitris C. Dracopoulos)
63. University of Wisconsin Machine Learning (Jude Shavlik)
64. University of Wisconsin Medical School: Statistical Relational Learning (David Page, Mark Craven, Jude Shavlik)

Return to Educator Courses Resource page

© 2008 Robert Fisher