CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos
Challenge Midterm Final
Problem Sets
1. Counting 2. Core Probability 3. Random Variables 4. Probabilistic Models 5. Models, Beta, and CLT 6. Uncertainty Theory 7. Machine Learning
Lecture
1. Welcome 2. Combinatorics 3. Probability 4. Conditioning and Bayes 5. Independence 6. Random Variables & Binomial 7. Moments 8. Poisson 9. Continuous 10. Gaussian 11. Probabilistic Models 12. Inference 13. General Inference 14. Multinomial 15. Beta 16. Adding Variables 17. Sampling 18. Bootstrapping 19. Algorithm Analysis 20. Information Theory 21. Maximum Likelihood Estimation 22. Logistic Regression 23. Comparing Classifiers 24. Beyond Classification 25. Deep Learning 26. Diffusion
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8 Section 9
Section Reassign Form
PEP
Midterm Final

Schedule

Lecture 19: Algorithm Analysis

Feb 24, 2025
Nvidia Auditorium, 3p

Lecture Materials

Learning Goals

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