CS 109: Probability for Computer Scientists, Summer 2021

Announcements and Updates

  • [6/21] Welcome to CS109! The course is open for shopping on Canvas - instructions on how to add yourself to Gradescope/Edstem are on the front page there. Please email us if you are having trouble accessing any materials.

Schedule

Week 1
Topic
Materials
Assignments
Week 1
Lecture 1
(Mon, Jun 21)
1.1 So You Think You Can Count?
1.2 More Counting
(Tues, Jun 22)
Lecture 2
(Wed, Jun 23)
1.3 No More Counting Please
(Thu, Jun 24)
LaTeX and Python Tutorials, 5:30pm PDT
Lecture 3
(Fri, Jun 25)
2.1 Discrete Probability
9.2 Probability via Simulation [PSet 1 Coding]
syllabus cc due 11pm PT
Week 2
Lecture 4
(Mon, Jun 28)
2.2 Conditional Probability
2.3 Independence
0.3 cc (extra credit) due 11pm PT
(Tues, Jun 29)
Lecture 5
(Wed, Jun 30)
3.1 Discrete Random Variables Basics
9.3The Naive Bayes Classifier [PSet 2 Coding]
(Thu, Jul 1)
Lecture 6
(Fri, Jul 2)
3.2 More on Expectation
3.3 Variance
Week 3
No Lecture
(Mon, Jul 5)
No Class - July 4th Holiday
  • PSet 1 due 11pm PT
(Tues, Jul 6)
Lecture 7
(Wed, Jul 7)
3.4 Zoo of Discrete RVs I
3.5 Zoo of Discrete RVs II
(Thu, Jul 8)
Lecture 8
(Fri, Jul 9)
3.6 Zoo of Discrete RVs III
Week 4
Lecture 9
(Mon, Jul 12)
4.1 Intro Continuous Random Variables
(Tues, Jul 13)
Mini-Quiz 1
Lecture 10
(Wed, Jul 14)
4.2 Zoo of Continuous Random Variables
9.4 Bloom Filter [PSet 3 Coding]
(Thu, Jul 15)
Lecture 11
(Fri, Jul 16)
4.3 Normal Distribution
4.4 Transforming Random Variables
Week 5
Lecture 12
(Mon, Jul 19)
5.1 Joint Discrete Random Variables
(Tues, Jul 20)
Lecture 13
(Wed, Jul 21)
5.2 Joint Continuous Random Variables
(Thu, Jul 22)
Lecture 14
(Fri, Jul 23)
5.3 Conditional Distributions & Conditional Expectation
9.5 Distinct Elements [PSet 3 Coding]
Week 6
Lecture 15
(Mon, Jul 26)
5.4 Covariance & Correlation
(Tues, Jul 27)
Lecture 16
(Wed, Jul 28)
5.5 Convolution
(Thu, Jul 29)
Lecture 17
(Fri, Jul 30)
5.6 Moment Generating Functions
5.7 Limit Theorems
5.11 Proof of the CLT
5.6 slides notes cc
5.7 slides notes cc
5.11 slides notes [no cc] (optional)
problems solutions
Week 7
Lecture 18
(Mon, Aug 2)
5.8 Multinomial and Multivariate Hypergeometric
5.9 Multivariate Normal
5.10 Order Statistics
5.8 slides notes cc
5.9 slides notes [no cc] (optional)
5.10 slides notes [no cc] (optional)
problems solutions
(Tues, Aug 3)
Mini-Quiz 2
Lecture 19
(Wed, Aug 4)
9.6 Markov Chain Monte Carlo [PSet 4 Coding]
9.6 slides notes [no cc]
(Thu, Aug 5)
Lecture 20
(Fri, Aug 6)
7.1 Maximum Likelihood
7.2 Maximum Likelihood Examples
Week 8
Lecture 21
(Mon, Aug 9)
7.3 Method of Moments
7.4 Beta and Dirichlet
(Tues, Aug 10)
Lecture 22
(Wed, Aug 11)
7.5 Maximum a Posteriori
7.6 Properties of Estimators I
7.7 Properties of Estimators II
7.8 Properties of Estimators IIII
7.5 slides notes (cc is same as 7.4)
7.6 slides notes cc (extra credit)
7.7 slides notes (cc is same as 7.6)
7.8 slides notes (cc is same as 7.6)
problems solutions
(Thu, Aug 12)
Lecture 23
(Fri, Aug 13)
Embedded Ethics
Guest Lecturer Katie Creel
slides no cc
Week 9
Lecture 24
(Mon, Aug 16)
8.1 Confidence Intervals
8.2 Credible Intervals
8.1 slides notes cc
8.2 slides notes (cc is same as 8.1)
problems solutions
(Tues, Aug 17)
Lecture 25
(Wed, Aug 18)
8.3 Hypothesis Testing
9.7 Bootstrapping [PSet 5 Coding]
(Thu, Aug 19)
Lecture 26
(Fri, Aug 20)
9.8 Multi-Armed Bandits [PSet 5 Coding]
9.8 slides notes [no cc]
Week 10
Lecture 27
(Mon, Aug 23)
Logistic Regression
slides no cc
(Tues, Aug 24)
Mini-Quiz 3
Lecture 28
(Wed, Aug 25)
Linear Regression
slides no cc
6.1 slides notes cc (extra credit)
6.2 slides notes (cc is same as 6.1)
6.3 slides notes (cc is same as 6.1)
(Thu, Aug 26)
Lecture 29
(Fri, Aug 27)
How to Lie with Statistics
slides no cc
  • PSet 5 In

Note that content for future lectures is subject to change.

This course website heavily follows the example of the website of CSE373 2019 Spring at the University of Washington, Seattle.