Office hours: Tuesday 1-2 PM
Piazza webpage


Sept 6 Homework 1 is up (due before class on Tuesday, Sept 13) HW1
Sept 13 Homework 2 is up (due before midnight Sunday, Sept 25) HW2
Sept 27 Homework 3 is up (due before midnight Sunday, Oct 9) HW3
Oct 10 Homework 4 is up (due before midnight Sunday, Oct 23) HW4
Oct 15 Earlier midterms 2015 2014
Oct 27 Homework 5 is up (due before class Thursday, Nov 10) HW5
Nov 10 Homework 6 is up (due before class Thursday, Nov 24) HW6
Nov 21 Earlier midterms 2015
Dec 3 Midterm 2 pdf


Week 1 Iain Murray's cribsheet, Sam Roweis' notes, A short introduction to R , Plotting with ggplot2
Useful references: Review of probability and statistics (Stanford notes) , R Manual
Week 2 R Markdown: Tutorial 1 , Tutorial 2 (we will use this for the homeworks)
Section 5 on eigenvalues of Richard Shewchuk's tutorial on the conjugate gradient method
Week 3 Complexity and big-O notation: Link 1 , Link 2 Link 3
This is a nice introduction to big-O notation
Knapsack problem (ignore the C code)
The Needleman-Wunsch algorithm
Week 4 Clustering: Read Section 10.3 of An Introduction to Statistical Learning
MLE for the multivariate Gaussian. See Sections 2 and 3 here .
Week 5 MLE for the multivariate Gaussian. See Sections 2 and 3 here .
Jensen's inequality here . Kullback-Liebler divergence (Page 2 here ).
Week 6 Tony Jebara's' introduction to exponential family distributions.
Jeff Bilmes' tutorial on the EM algorithm.
If you're feeling ambitious, here is the original paper on the EM algorithm.

Some project ideas here , I'll try to add to this. You can search for others on other course webpages. In general, make sure your project allows you to code up some non-trivial algorithm (rather than just use packages)
Week 7 Some notes on gradient descent here . This is also useful.
Week 9 Jeff Gills's introduction to MCMC.
Andrieu et al.'s introduction to MCMC.
Radford Neal, in rather more detail on MCMC.