- Huge thanks to all prior instructors of this course at UW, the staff of Berkeley CS 70, Mor Harchol-Balter and Ryan O'Donnell. I have borrowed ideas, problems, and presentation ideas from all of the above.

Week 1

Topic

Materials

Assignments

Week 1

Lecture 1

(Wed, Jan 4)

Introduction

So you think you can count?

So you think you can count?

Week 2

Lecture 4

(Wed, Jan 11)

Discrete Probability

Lecture 5

(Fri, Jan 13)

Conditional Probability

Bayes Theorem

Law of Total Probability

Bayes Theorem

Law of Total Probability

Week 3

Holiday

(Mon, Jan 16)

Lecture 6

(Wed, Jan 18)

Independence

Chain Rule

Chain Rule

Lecture 7

(Fri, Jan 20)

More independence

Intro Random Variables

Intro Random Variables

Week 4

Lecture 8

(Mon, Jan 23)

More Random Variables

Expectation

Intro Linearity of Expectation

Expectation

Intro Linearity of Expectation

Lecture 9

(Wed, Jan 25)

More linearity of expectation

LOTUS

LOTUS

Week 5

Lecture 11

(Mon, Jan 30)

More Independence of r.v.s

Application: Bloom Filters

Application: Bloom Filters

Lecture 12

(Wed, Feb 1)

Zoo of Discrete RVs

Week 6

Week 7

Lecture 17

(Mon, Feb 13)

Lecture 18

(Wed, Feb 15)

Normal distributions

Central Limit Theorem

Central Limit Theorem

Lecture 19

(Fri, Feb 17)

Application: Polling

Intro to Confidence Intervals

Intro to Confidence Intervals

Week 8

Holiday

(Mon, Feb 20)

Lecture 20

(Wed, Feb 22)

Joint distributions

Recap polling

Recap polling

Lecture 21

(Fri, Feb 24)

Conditional Expectation

Law of Total Expectation

Law of Total Expectation

Week 9

Lecture 23

(Wed, Mar 1)

Maximum Likelihood Estimation

- Pset7 Due 11:59pm PDT

Pset 8 PDF Pset 8 LaTeX Template

Pset 8 Extra Credit LaTeX Template

Week 10

(Sat, Mar 11)

- Extra Credit (MCMC for Knapsack) Due 11:59pm PDT

Finals Week

This course website heavily follows the example of the website of CSE373 2019 Spring.