After each lecture, you can download the videos
or watch them in youtube:
The lectures for 340, the undergrad version of this course, are in youtube:
undergraduate machine learning
. I recommend you watch these prior to the 540 class.
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 19. Spring break.
Th Feb 21. Spring 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 2. Exam Revision.
Th Apr 4. Exam.
Tue Apr 16. Project due.
Tue Apr 23. Reviews due.