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  
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 bigO notation: Link 1 ,
Link 2
Link 3 This is a nice introduction to bigO notation Quicksort Knapsack problem (ignore the C code) The NeedlemanWunsch 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 . KullbackLiebler 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 nontrivial 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. 