Schedule

Date Topic Notes Reading Assignment out/due
1/9 Introduction Course Introduction
Murphy 1
1/11 Linear regression Linear regression Murphy 7-7.5.1 PA 1 out
1/16 Logistic regression
Murphy 8-8.3
1/18 Probability refresher
Murphy 2, 3.1-3.2
1/23 Generative models
Murphy 3
1/25 Perceptrons
Murphy 8.5.4, 14.1-14.2, 14.4-14.5 PA 1 due
1/30 SVMs and Kernels
Murphy 14.1-14.2, 14.4-14.5 PA 2 out
2/1 SVMs and Kernels


2/6 Decision trees
Murphy 16.1 -- 16.6
2/8 Neural nets
Murphy 16.5 PA 2 due 2/12
2/13 Neural nets
(optional: Bishop 5) 
2/15 Deep learning
Murphy 28 PA 3 out
2/20 Midterm 1


2/22 Mixture models and EM
Murphy 11
2/27 Dimensionality reduction
Murphy 12 Project proposal due
3/1 Clustering
Murphy 25.1, 25.5
 PA 3 due
3/6 No Class: Spring Break


3/8 No Class: Spring Break


3/13 [ Snow day ]


3/15 Gaussian processes
Murphy 15.1-15.2
3/20 Graphical models
Murphy 10.1, 10.3-10.5
3/22 Markov models
Murphy 17.1-17.5 PA4 out
3/27 Markov decision processes (MDPs)
SB 3
3/29 Planning with MDPs
SB 4
4/3 Reinforcement learning
SB 1, SB 6
4/5 Reinforcement learning
SB 9 PA 4 Due
4/10 Midterm 2


4/12 Project Presentations


4/17 Project Presentations


4/24 Project Reports Due

Project report due at 11:59 PM -- This is a hard deadline, no extensions


Important note: unless noted otherwise, all readings and assignments are due on the day that they appear in the schedule.

Unless noted otherwise, all readings are from M: Murphy Machine Learning A Probabilistic Perspective or SB: Sutton and Barto Reinforcement Learning: An Introduction, 2nd Edition.