CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos Language Model Tool Handout Midterm
Problem Sets
1. Core Probability 2. Discrete Random Variables 3. Continuous Random Variables 4. Probabilistic Models 5. Uncertainty Theory (part 1)
Lecture
1. Welcome 2. Conditioning and Bayes 3. Independence 4. Binomial 5. Moments 6. Poisson 7. Continuous 8. Gaussian 9. Probabilistic Models 10. Inference 11. General Inference 12. Multinomial 13. Beta 14. Central Limit Theorem 15. Review 16. Bootstrapping
Section
Section 1 Section 2 Section 3 Section 4 Section 5
Section Reassign Form
PEP
Midterm

Schedule

Lecture 19: Algorithm Analysis

Feb 24, 2025
Hewlett 200, 3p

Lecture Materials

Learning Goals

Learn about the Law of Total Expectation, and using it to analyze code.