Handouts

• To review the very basics of probability theory, you may read Chapter 1 of these lecture notes by Matousek and Vondrak. For a slightly more elaborate review (though still without measure theory), you may read this note for a machine learning course at Stanford.
  Warning: If the notions in these reviews on probability theory are mostly new to you, you should consider dropping this class, or at least have a discussion with the instructor. Unlike the other sources in this page, these reviews are not supposed to be tutorials.
• Read Michel Goeman's notes for a good review of linear programming.

Problem Sets

• Problem Set 1, due on Thursday, October 12.
• Problem Set 2, due on Tuesday, October 31.
• Problem Set 3, due on Thursday, November 9.
• Problem Set 4, due on Thursday, December 1.
• Final exam , due on December 7, at 10am.

Exercises

This list of exercises will be updated regularly throughout the course. These exercises are more straightforward applications of main ideas or concepts covered in the lectures. They are less involved than those in the problem sets, and should be used to test and stregnthen the students' basic understanding of the materials. Solutions are not turned in or graded, but discussions are welcome on Piazza. Answers to some computational questions are provided at the end of the list.