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27 Jan 2023: David MacKay's Book, Chapters 20, 22 and 23 on k-means and MoG. ... Max Welling's Class Notes on PCA and FAOct 20 and 23.

19 Nov 2023: Jan 20, 23. Unsupervised learning (2L): factor analysis, PCA, the EM algorithm. ... Feb 17, 20. Variational approximations (2L): KL divergences, mean field, expectation propagation.

27 Jan 2023: Suggested Further Readings:. David MacKay's Book, Chapters 20, 22 and 23 on k-means and MoG. ... Hard copies will be made available in class. Max Welling's Class Notes on PCA and FAOct 17 and 20.

19 Nov 2023: Jan 16, 20. Introduction to Machine Learning(2L):. the concept of a model, linear in the parameters regression: makaing predictions, least squares fit, overfitting. ... Feb 3, 6, 10, 13. Probabilistic Ranking. Feb 17, 20, 24, 27, Mar 3, 6.

27 Jan 2023: Course Web Page. Time: Mondays and Wednesdays 1:00 pm - 2:20 pm. ... Week 10 /11 (March 18, 20, 25, 27). Latent Variable Time Series Models.

19 Nov 2023: Feb 20, 22. Variational approximations (2L): KL divergences, mean field, expectation propagation.

27 Jan 2023: Online Prediction - Bayes versus Experts 7:00 dinner ---------------------------------------------------------------------------------------------------- Day 2 - Tuesday 20 July 9:00 - 10:30 John Langford The precise role of "bias" or "prior" in

19 Nov 2023: Jan 26, 30 and Feb 2nd. Gaussian processes (3L):. Feb 6, 9, 13, 16 and 20.

27 Jan 2023: Bioinformatics, 20: 1361-1372. Bourne, P.E., Allerston, C.K.J, Krebs, W., Li, W, Shinyalov, I.N., Godzik, A., Friedberg, I., Liu, T., Wild, D.L., Hwang, S., Ghahramani, Z.,

19 Nov 2023: LECTURE SYLLABUS. Jan 20 . Introduction to Machine Learning(1L): review of probabilistic models, relation to coding terminology: Bayes rule, supervised, unsupervised and reinforcement learning.