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The lecture notes are available here.

1 Aug 26 Course Information HTML
2 Aug 26 Project Guidelines HTML
3 Aug 31 Linear Algebra review PDF
4 September 2 Homework 1, due Sep 21 PDF.
Data: NN, NN-NoMatlab, regression,
5 September 8 Optional reading: Cover Trees for Nearest Neighbor PDF
7 Sep 28 Homework 2, due Oct 8 PDF
Data:,,, NN, NN-NoMatlab. (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 PDF
11 Homework 4, due Dec 3 pdf
Data: discretemrf-cs6780.txt
12 Reviewing instructions, due Dec 10 Review Instructions

Other resources

Matrix Cookbook
Here are a couple of Matlab tutorials that you might find helpful:
Matlab tutorial 1
Matlab tutorial 2
Examples of machine learning algorithms.
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.