CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos AIWG Student Guide
Midterm 1 Challenge
Problem Sets
1. Core Probability 2. Discrete Random Variables 3. Continuous Random Variables 4. Probabilistic Models 5. Uncertainty Theory 6. Machine Learning and Logistic Regression
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. Sampling & Bootstrapping 17. Algorithm Analysis 18. Information Theory 19. Maximum Likelihood Estimation 20. Logistic Regression 21. Comparing Classifiers 22. Deep Learning

Schedule

Section 6

May 14th, 2026

This week we'll get practice with Algorithmic Analysis, and Central Limit Theorem.

Section Materials

Section Handout