$\DeclareMathOperator{\p}{Pr}$ $\DeclareMathOperator{\P}{Pr}$ $\DeclareMathOperator{\c}{^C}$ $\DeclareMathOperator{\or}{ or}$ $\DeclareMathOperator{\and}{ and}$ $\DeclareMathOperator{\var}{Var}$ $\DeclareMathOperator{\E}{E}$ $\DeclareMathOperator{\std}{Std}$ $\DeclareMathOperator{\Ber}{Bern}$ $\DeclareMathOperator{\Bin}{Bin}$ $\DeclareMathOperator{\Poi}{Poi}$ $\DeclareMathOperator{\Uni}{Uni}$ $\DeclareMathOperator{\Exp}{Exp}$ $\DeclareMathOperator{\N}{N}$ $\DeclareMathOperator{\R}{\mathbb{R}}$ $\newcommand{\d}{\, d}$

Schedule

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.

Overview of Topics


Counting Theory

Core Probability

Random Variables

Probabilistic Models

Uncertainty Theory

Machine Learning

Lecture Plan

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

1
#WeekdayDateTopicNotes
2
Week 1
3
1MondayJan 6Counting
4
2WednesdayJan 8Combinatorics
5
3FridayJan 10What is Probability?
6
Week 2
7
4MondayJan 13Conditional Probability and Bayes
8
5WednesdayJan 15IndependencePSet 1 Due
9
6FridayJan 17Random Variables and Binomial
10
Week 3
11
-MondayJan 20No Class (MLK Jr Day)
12
7WednesdayJan 22Moments
13
8FridayJan 24PoissonPSet 2 Due
14
Week 4
15
9MondayJan 27Continuous Random Variables
16
10WednesdayJan 29Normal Distribution
17
11FridayJan 31Probabilistic ModelsPSet 3 Due
18
Week 5
19
12MondayFeb 3InferencePEP 1
20
13WednesdayFeb 5General Inference
21
14FridayFeb 7MultinomialPSet 4 Due
22
Week 6
23
-MondayFeb 10No Class (Break)
24
-TuesdayFeb 11MidtermMidterm: 7 - 9pm
25
15WednesdayFeb 12Beta
26
16FridayFeb 14Central Limit Theorem
27
Week 7
28
-MondayFeb 17No Class (President's Day)
29
17WednesdayFeb 19Sampling Statistics
30
18FridayFeb 21Bootstraping and P-ValuesPSet 5 Due
31
Week 8
32
19MondayFeb 25Algorithmic Analysis
33
20WednesdayFeb 27Information Theory
34
21FridayFeb 28M.L.E
35
Week 9
36
22MondayMar 3OptimizationPSet 6 Due
37
23WednesdayMar 5Logistic Regression
38
24FridayMar 7Comparing Classifiers
39
Week 10
40
25MondayMar 10Beyond ClassificationPEP 2
41
26WednesdayMar 12Deep Learning
42
27FridayMar 14Final LecturePSet 7 Due

Readings

This quarter we are writing a new Course Reader for CS109 which is free and written for the course. You can access the previous course reader Fall 2024 Course ReaderYou 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.