Tentative Course Schedule

PRML = Pattern recognition and machine learning by Christopher Bishop

--Date-- TopicReadingWhat's due
Jan 14 Introduction to machine learning
PRML 1, 2.1-2.4

Supervised learning
Jan 16
Linear models for regression
PRML 3.1,3.2
Jan 21
Linear models for regression PRML 3.3-3.6 Form project groups (of two) and email devika@rice.edu, PA1 due at 8 pm.
Jan 23 Linear models for classification PRML 4.1,4.3 Project proposal due Jan 24 on blogs.rice.edu (send URL to devika@rice.edu), HW1 due Jan 24 at 8 pm.
Jan 28 Linear models for classification PRML 4.2,4.5
Jan 30 Kernel methods PRML 6.1-6.3
Feb 4 Sparse kernel methods: SVMs PRML 7.1
Feb 6 Sparse kernel methods: SVMs PRML 7.1 Progress report 1 due Feb 7 on blogs.rice.edu
Feb 11 Adaptive basis functions: neural networks PRML 5.1-5.3
Feb 13 Adaptive basis functions: neural networks PRML 5.4,5.5,5.7
Feb 18 Graphical models: bayesian networks PRML 8.1-8.2
Feb 20 Graphical models: MRFs PRML 8.3 Progress report 2 due Feb 21 on blogs.rice.edu
Feb 25 Ensemble models PRML 14.1-14.3
Feb 27 Ensemble models PRML 14.4-14.5 Exam 1 from 7 pm to 10 pm (location TBA)
Mar 4 Spring break

Mar 6 Spring Break
Mar 11 Gaussian Processes PRML 6.4
Mar 13 Gaussian Processes PRML 6.4 Progress report 3 due Mar 14 on blogs.rice.edu
Unsupervised learning
Mar 18 Clustering and k-means PRML 9.1
Mar 20 Expectation maximization and mixtures of Gaussians PRML 9.2-9.3
Mar 25 Latent linear models PRML 12.1-12.3
Mar 27 Latent non-linear models PRML 12.4 Progress report 4 due on Mar 28 on blogs.rice.edu
Sequential learning
Apr 1 Hidden Markov models PRML 13.1-13.2
Apr 3 Spring Recess
Apr 8 Hidden Markov models PRML 13.1-13.2
Apr 10 Reinforcement learning Progress report 5 due on April 11 on blogs.rice.edu
Apr 15 Reinforcement learning
Apr 17 Reinforcement learning Draft final report due on April 18 on blogs.rice.edu
Apr 22 Course wrap up
Apr 24 Final poster presentations Final project report due on Apr 25 on blogs.rice.edu