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 20  Counting  
4

2  Wednesday  Sept 22  Combinatorics  PSet 1 out 
5

3  Friday  Sept 24  What is Probability?  
Week 2  
7

4  Monday  Sept 27  Conditional Probability and Bayes  
8

5  Wednesday  Sept 29  Independence  
9

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

7  Monday  Oct 4  Variance Bernoulli Binomial  
12

8  Wednesday  Oct 6  Poisson  
13

9  Friday  Oct 8  Continuous Random Variables  
Week 4  
15

10  Monday  Oct 11  Normal Distribution  PSet 2 in / PSet 3 out 
16

11  Wednesday  Oct 13  Joint Distributions  
17

12  Friday  Oct 15  Continuous Joint  
Week 5  
19

13  Monday  Oct 18  Inference  
14  Wednesday  Oct 20  Modelling  
15  Friday  Oct 22  General Inference  PSet 3 in  
Week 6  
16  Monday  Oct 25  No Class  
  Tuesday  Oct 26  Midterm  Midterm: 7  9pm  
17  Wednesday  Oct 27  Mixture Models  PSet 4 out  
18  Friday  Oct 29  Central Limit Theorem  
Week 7  
19  Monday  Nov 1  Great Expectations  
20  Wednesday  Nov 3  Bootstraping and PValues  
21  Friday  Nov 5  Beta Distributions  PSet 4 in / PSet 5 out  
Week 8  
22  Monday  Nov 8  M.L.E.  
23  Wednesday  Nov 10  M.A.P.  
24  Friday  Nov 12  Naive Bayes 
Withdraw deadline


Week 9  
25  Monday  Nov 15  Ethics in Machine Learning  PSet 5 in / PSet 6 out  
26  Wednesday  Nov 17  Optimization  
27  Friday  Nov 19  Logistic Regression  
Week 10  
28  Monday  Nov 29  Deep Learning  Challenge in  
29  Wednesday  Dec 1  Future of Probability  PSet 6 in  
30  Friday  Dec 3  No Class  Final: Thurs, Dec 9th, 12:15  3:15pm 
This quarter we are writing a Course Reader for CS109 which is free (but will be constructed as we go)! 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.