CS109
Course Resources
Syllabus
Schedule
Honor Code
Office Hours
Course Reader
Python Review
Latex Cheat Sheet
Lecture Videos
AIWG Student Guide
Challenge
Midterm
Problem Sets
1. Core Probability
2. Discrete Random Variables
3. Continuous Random Variables
4. Probabilistic Models
5. Uncertainty Theory
6. Information Theory + MLE
Music Voting
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Lecture
1. Welcome
2. Conditioning and Bayes
3. Independence
4. Counting
5. Binomial
6. Moments
7. Poisson
8. Continuous
9. Gaussian
10. Probabilistic Models
11. Inference
12. General Inference
13. Multinomial
14. Beta
15. Central Limit Theorem
16. Algorithm Analysis
17. Information Theory
18. Sampling & Bootstrapping
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Section
Section 1
Section 2
Section 3
Section 4
Section 5
Section 6
Section Reassign Form
Times and Locations
PEP
Midterm
Schedule
Lecture 16: Algorithm Analysis
Feb 13 2026
Hewlett 200, 3p
Lecture Materials
Slides PDF
Lecture Questions
Lecture Solutions
Reading
Learning Goals
Learn about the Law of Total Expectation, and using it to analyze code.