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

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 Jun 22 What is Probability?
4
 2 Tue Jun 23 Conditional Probability Out: PSet #1
5
 3 Wed Jun 24 Bayes Theorem
6
 4 Thu Jun 25 Counting and Combinitorics
7
 Week 2
8
 5 Mon Jun 29 Random Variables and Expectation
9
 6 Tue Jun 30 Moments Due: Pset #1 / Out: PSet #2
10
 7 Wed Jul 1 Poisson
11
 8 Thu Jul 2 Continuous Random Variables
12
 Week 3
13
 9 Mon Jul 6 Normal Distribution Due: Pset #2 / Out: Pset #3
14
 10 Tue Jul 7 Probabilistic Models
15
 11 Wed Jul 8 Beta
16
 12 Thu Jul 9 General Inference Due: Pset #3 / Out: Pset #4
17
 Week 4
18
 - Mon Jul 13 Midterm 1 Midterm in-class
19
 13 Tue Jul 14 Multinomial
20
 14 Wed Jul 15 Beta
21
 15 Thu Jul 16 TBD Due: Pset #4 / Out: Pset #5
22
 Week 5
23
 16 Mon Jul 20 Central Limit Theorem + SEM
24
 17 Tue Jul 21 Algorithmic Analysis Midterm
25
 18 Wed Jul 22 Bootstrapping and P-Values
26
 19 Thu Jul 23 Algorithm Analysis
27
 Week 6
28
 20 Mon Jul 27 Information Theory
29
 21 Tue Jul 28 MLE Due: Pset #5 / Out: Pset #6
30
 22 Wed Jul 29 Logistic Regression
31
 23 Thu Jul 30 Comparing Classifiers
32
 Week 7
33
 24 Mon Aug 3 Deep Learning
34
 - Tue Aug 4 Midterm 2 Midterm in-class
35
 25 Wed Aug 5 Ethics
36
 26 Thu Aug 6 Beyond Classification
37
 Week 8
38
 27 Mon Aug 10 Applications / Practice Due: Pset #7
39
 28 Tue Aug 11 Beyond CS109
40
 29 Wed Aug 12 Review Session
41
 - Sat Aug 15 Final Exam 8:30-11:30am

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.

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