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.

Note on videos: By popular demand we are making videos of CS109 classes. Our intention is for the videos to be an extra feature we produce as staff to make the class a better experience (were all about the better experience). This quarter CS109 is not "SCPD" so we are not professionally recorded. You can expect for videos to be uploaded within 24 hours of class. The videos are hosted by YouTube but are unlisted (only people with the link can find them).
Note on slides: In class I sometimes use slides to facilitate lecture. On their own they are not a great study resource. Here is a link to the slides directory in case you have use for them.


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

Date Topic Files Reading Assignment

Mar 28 Counting Chapter 1.1 - 1.2
Mar 30 Permutations and Combinations Chapter 1.3 - 1.6

[Core Probability]
Apr 1 Axioms of Probability Chapter 2.1-2.5, 2.7
Apr 4 Conditional Probability, Bayes Theorem Chapter 3.1-3.3
Apr 6 Independence Chapter 3.4-3.5
Apr 8 Random Variables, Expectation Chapter 4.1-4.4 Due: PSet #1

Apr 11 Variance, Bernoulli and Binomial Chapter 4.5-4.6
Apr 13 Discrete Distributions Chapter 4.7-4.10
Apr 15 Continuous Random Variables Chapter 5.1-5.3
Apr 18 Normal Distributions Chapter 5.4-5.6 Due PSet #2
Apr 20 Joint Distribution Functions Chapter 6.1
Apr 22 Independent Random Variables Chapter 6.2-6.3
Apr 25 Conditional Distributions Chapter 6.4-6.5
Apr 27 Beta Distributions Chapter 7.1-7.2 Due: PSet #3

[Advanced Probability]
Apr 29 Variance From Events Chapter 7.3-7.4
May 2 Covariance and Samples Chapter 7.5-7.6
May 4 Correlation Chapter 7.7
May 6 Conditional Expectation Chapter 8.1-8.2, 8.5
May 9 Central Theorems Chapter 8.3-8.4 Due PSet #4

[Machine Learning]
May 11 Parameters and Learning Lecture Handout
May 13 Maximizing Likelihood Lecture Handout
May 16 Maximum A Posteriori Lecture Handout
May 18 Naive Bayes Lecture Handout Due PSet #5
May 20 Logistic Regression Lecture Handout
May 23 Deep Learning Lecture Handout
May 25 Applied Machine Learning None
May 27 CS109 Overview None
May 30 Memorial Day (no class)
Jun 1 Dead Day (no class) Due PSet #6