The class starts by providing a fundamental grounding in combinatorics, and then quickly moves into the basics of probability theory. We will then cover many essential concepts in probability theory, including particular probability distributions, properties of probabilities, and mathematical tools for analyzing probabilities. Finally, the last third of the class will focus on data analysis and machine learning as a means for seeing direct applications of probability in this exciting and quickly growing subfield of computer science.

The schedule is subject to change by the management at any time.

Week | Monday | Wednesday | Friday |
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1 | |||

2 | |||

3 | |||

4 | |||

5 |
Chapter 5.6.1, 5.6.4 Jul 26th Conditional Distributions,Beta Distribution |
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6 |
Aug 2nd Samples and Bootstrapping |
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7 | |||

8 |
Due: PSet #6 Lecture notes Aug 16th Deep Learning Intro,CS 109 Overview |
Aug 19th (Saturday) Final exam: Sat 12:15-3:15pm |