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

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


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
 Lecture Day Date Topic Notes
2
 Week 1
3
 1 Mon Mar 30 What is Probability?
4
 2 Wed Apr 1 Conditional Probability Out: PSet #1
5
 3 Fri Apr 3 Bayes Theorem
6
 Week 2
7
 4 Mon Apr 6 Counting and Combinitorics
8
 5 Wed Apr 8 Random Variables and Expectation
9
 6 Fri Apr 10 Moments Due: Pset #1 / Out: PSet #2
10
 Week 3
11
 7 Mon Apr 13 Poisson
12
 8 Wed Apr 15 Continuous Random Variables
13
 9 Fri Apr 17 Normal Distribution Due: Pset #2 / Out: Pset #3
14
 Week 4
15
 10 Mon Apr 20 Probabilistic Models
16
 11 Wed Apr 22 Beta
17
 12 Fri Apr 24 General Inference Due: Pset #3 / Out: Pset #4
18
 Week 5
19
 13 Mon Apr 27 Multinomial
20
 14 Wed Apr 29 Beta
21
 15 Fri May 1 Midterm Review Due: Pset #4 / Out: Pset #5
22
 Week 6
23
 16 Mon May 4 Central Limit Theorem + SEM
24
 - Wed May 6 Midterm (No Lecture) Midterm 7:00pm
25
 17 Fri May 8 Information Theory
26
 Week 7
27
 18 Mon May 11 Bootstrapping and P-Values
28
 19 Wed May 13 MLE
29
 20 Fri May 15 Logistic Regression Due: Pset #5 / Out: Pset #6
30
 Week 8
31
 21 Mon May 18 Comparing Classifiers
32
 22 Wed May 20 Deep Learning
33
 23 Fri May 22 Beyond Classification
34
 Week 9
35
 - Mon May 25 Memorial Day Due: Pset #6 / Out: Pset #7
36
 24 Wed May 27 Ethics
37
 25 Fri May 29 Applications / Practice
38
 Week 10
39
 26 Mon Jun 1 Applications / Practice Due: Pset #7
40
 27 Wed Jun 3 Beyond CS109
41
 28 Wed Jun 3 Review Session
42
 - Mon Jun 8 Final Exam 3:30-6:30pm

Readings

This course has a Course Reader for CS109 which is free and written for the course. You can also 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.

'