Handouts
The lecture notes are available here.
#  DATE  TOPIC  LINK 

1  Aug 26  Course Information  HTML 
2  Aug 26  Project Guidelines  HTML 
3  Aug 31  Linear Algebra review  
4  September 2  Homework 1, due Sep 21  PDF. Data: NN, NNNoMatlab, regression, regressionNoMatlab. 
5  September 8  Optional reading: Cover Trees for Nearest Neighbor  
7  Sep 28  Homework 2, due Oct 8  PDF Data: astrophysics.zip, facedata.zip, facecode.zip, NN, NNNoMatlab. (If it applies, you can do the programming part of SVm question on your project's dataset.) 
9  Oct 18  Homework 3, due Nov 11  PDF 
10  Oct 27  Midterm Survey  
11  Homework 4, due Dec 3  pdf Data: discretemrfcs6780.txt 

12  Reviewing instructions, due Dec 10  Review Instructions 
Other resources
 Matrix
 Matrix Cookbook
 Matlab
 Here are a couple of Matlab tutorials that you might find helpful:
Matlab tutorial 1
Matlab tutorial 2
Examples of machine learning algorithms.  Data
 Here is the UCI Machine learning repository, which contains a large collection of standard datasets for testing learning algorithms. If you want to see examples of recent work in machine learning, start by taking a look at the conferences NIPS (all old NIPS papers are online) and ICML. Some other related conferences include UAI, AAAI, IJCAI.