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.
Lecture content is subject to change by the management at any time.
1

#  Weekday  Date  Topic  Notes 

Week 1  
3

1  Monday  Sept 26  Counting  
4

2  Wednesday  Sept 28  Combinatorics  PSet 1 out 
5

3  Friday  Sept 30  What is Probability?  
Week 2  
7

4  Monday  Oct 3  Conditional Probability and Bayes  
8

5  Wednesday  Oct 5  Independence  
9

6  Friday  Oct 7  Random Variables and Expectation  PSet 1 in / PSet 2 out 
Week 3  
11

7  Monday  Oct 10  Variance Bernoulli Binomial  
12

8  Wednesday  Oct 12  Poisson  
13

9  Friday  Oct 14  Continuous Random Variables  
Week 4  
15

10  Monday  Oct 17  Normal Distribution  PSet 2 in / PSet 3 out 
16

11  Wednesday  Oct 19  Joint Distributions  
17

12  Friday  Oct 21  Inference  
Week 5  
19

13  Monday  Oct 24  Inference II  
14  Wednesday  Oct 26  Modelling  
15  Friday  Oct 28  General Inference  PSet 3 in  
Week 6  
  Monday  Oct 31  No Class  
  Tuesday  Nov 1  Midterm  Midterm: 7  9pm  
16  Wednesday  Nov 2  Beta  PSet 4 out  
17  Friday  Nov 4  Adding Random Variables  
Week 7  
18  Monday  Nov 7  Central Limit Theorem  
19  Wednesday  Nov 9  Bootstraping and PValues  
20  Friday  Nov 11  Algorithmic Analysis  PSet 4 in / PSet 5 out  
Week 8  
21  Monday  Nov 14  M.L.E.  
22  Wednesday  Nov 16  M.A.P.  
23  Friday  Nov 18  Naive Bayes 
Withdraw deadline


Week 9  
24  Monday  Nov 28  Logistic Regression  PSet 5 in / PSet 6 out  
25  Wednesday  Nov 30  Deep Learning  
26  Friday  Dec 2  Fairness  
Week 10  
27  Monday  Dec 5  Advanced Probability  Challenge in  
28  Wednesday  Dec 7  Future of Probability  PSet 6 in  
29  Friday  Dec 9  No Class  Final: Thurs, Dec 13th, 8:30  11:30am 
This quarter we are writing a Course Reader for CS109 which is free and written for the course. You can optionally read from Sheldon Ross, A First Course in Probability (10th Ed.), Prentice Hall, 2018. The corresponding readings can be found Win 21 schedule. The textbook's 8th and 9th editions have the same readings and section headers.