CS 109: Probability for Computer Scientists, Summer 2021

Announcements and Updates

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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
1.1 slides notes cc
1.2 slides notes cc
PSet 1 Out:
PDF LaTeX Template/Starter Code
(Tues, Jun 22)
Lecture 2
(Wed, Jun 23)
1.3 No More Counting Please
1.3 slides notes cc
problems solutions
(Thu, Jun 24)
LaTeX and Python Tutorials, 5:30pm PDT
LaTeX Tutorial Python Tutorial
Lecture 3
(Fri, Jun 25)
2.1 Discrete Probability
9.2 Probability via Simulation [PSet 1 Coding]
2.1 slides notes cc
9.2 slides notes lesson no cc
problems solutions
syllabus cc due 11pm PT
Week 2
Lecture 4
(Mon, Jun 28)
2.2 Conditional Probability
2.3 Independence
2.2 slides notes cc
2.3 slides notes cc
problems solutions
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]
3.1 slides notes cc
9.3 slides notes lesson no cc
problems solutions
(Thu, Jul 1)
slides
Lecture 6
(Fri, Jul 2)
3.2 More on Expectation
3.3 Variance
3.2 slides notes cc
3.3 slides notes cc
problems solutions
PSet 2 Out
PDF LaTeX Template/Starter Code
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
3.4 slides notes cc
3.5 slides notes cc
problems solutions
(Thu, Jul 8)
Lecture 8
(Fri, Jul 9)
3.6 Zoo of Discrete RVs III
3.6 slides notes cc
problems solutions
Week 4
Lecture 9
(Mon, Jul 12)
4.1 Intro Continuous Random Variables
4.1 slides notes cc
problems solutions
(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]
4.2 slides notes cc
9.4 slides notes lesson no cc
problems solutions
(Thu, Jul 15)
slides
Lecture 11
(Fri, Jul 16)
4.3 Normal Distribution
4.4 Transforming Random Variables
4.3 slides notes cc
4.4 slides notes cc
problems solutions
Week 5
Lecture 12
(Mon, Jul 19)
5.1 Joint Discrete Random Variables
5.1 slides notes cc
problems solutions
(Tues, Jul 20)
Lecture 13
(Wed, Jul 21)
5.2 Joint Continuous Random Variables
5.2 slides notes cc
problems solutions
(Thu, Jul 22)
slides
Lecture 14
(Fri, Jul 23)
5.3 Conditional Distributions & Conditional Expectation
9.5 Distinct Elements [PSet 3 Coding]
5.3 slides notes cc
9.5 slides notes lesson no cc
problems solutions
Week 6
Lecture 15
(Mon, Jul 26)
5.4 Covariance & Correlation
5.4 slides notes cc
problems solutions
(Tues, Jul 27)
Lecture 16
(Wed, Jul 28)
5.5 Convolution
5.5 slides notes cc
problems solutions
(Thu, Jul 29)
slides
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)
slides
Lecture 20
(Fri, Aug 6)
7.1 Maximum Likelihood
7.2 Maximum Likelihood Examples
7.1 slides notes cc
7.2 slides notes [no cc]
problems solutions
Week 8
Lecture 21
(Mon, Aug 9)
7.3 Method of Moments
7.4 Beta and Dirichlet
7.3 slides notes cc
7.4 slides notes cc
problems solutions
(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)
slides
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]
8.3 slides notes cc
9.7 slides notes [no cc]
problems solutions
(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.