CS109

Course Resources
Syllabus Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet
Midterm Challenge Final
Problem Sets
1. Counting 2. Core Probability 3. Random Variables 4. Probabilistic Models 5. Uncertainty Theory
Lecture
1. Welcome 2. Combinatorics 3. Probability 4. Conditioning and Bayes 5. Independence 6. Random Variables 7. More Random Variables 8. Poisson 9. Continuous 10. Gaussian 11. Probabilistic Models 12. Inference 13. Modeling 14. General Inference & Beta 15. More Beta & Thompson Sampling 16. Adding Variables 17. Central Limit Theorem 18. Bootstrapping 19. Algorithm Analysis 20. MLE and MAP 21. Naive Bayes 22. Logistic Regressions 23. Deep Learning
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7

Schedule

Section 7

August 14th, 2024

This week we'll get practice with Algorithmic Analysis, Maximum Likelihood Estimation, and ML in general.

Section Materials

Section Handout