Full Course Playlist – Machine learning (CPSC-540 Nando de Freitas, UBC)

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These are the lectures for a graduate machine learning course taught by Nando de Freitas at UBC in 2013.

Professor: Nando de Freitas
University of British Columbia
Date: January, 2013

Course Schedule

Tue Jan 8. Introduction to machine learning. 

Th Jan 10. Linear prediction. 

Tue Jan 15. Maximum likelihood and linear prediction. 

Th Jan 17. Ridge, nonlinear regression with basis functions and Cross-validation.

Tue Jan 22. Ridge, nonlinear regression with basis functions and Cross-validation (continued). 

Th Jan 24. Bayesian learning (part I).

Tue Jan 29. Bayesian learning (part II). 

Th Jan 31. Gaussian processes for nonlinear regression (part I).

Tue Feb 5. Gaussian processes for nonlinear regression (part II).  Python demo code for GP regression.

Th Feb 7. Bayesian optimization, Thompson sampling and bandits. 

Tue Feb 12. Decision trees. 

Th Feb 14. Random forests. 

Tue Feb 19Spring break.

Th Feb 21Spring break.

Tue Feb 26. Random forests applications: Object detection and Kinect.

Th Feb 28. Unconstrained optimization: Gradient descent and Newton’s method. 

Tue Mar 5. Logistic regression, IRLS and importance sampling. 

Th Mar 7. Neural networks. 

Tue Mar 12. Deep learning with autoencoders. 

Th Mar 14. Deep learning with autoencoders II.

Tue Mar 19. Importance sampling and MCMC.

Th Mar 21. Importance sampling and MCMC.

Tue Mar 26. Importance sampling and MCMC.

Th Mar 28. Constrained optimization: Lagrangians and duality. Application to penalized maximum likelihood and Lasso. (notes from 2011 course – the lecture will actually be used to ask project questions.) 

Tue Apr 2Exam Revision.

Th Apr 4Exam.

Tue Apr 16Project due.

Tue Apr 23Reviews due.

 

(Source: Nando de Freitas | University of British Columbia)

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