CS109

Course
Syllabus Course FAQ What is CS109? Honor Code Policy
Staff / Office Hours
Problem Sets
1. Counting 2. Core Probability 3. Random Variables 4. Joint Variables 5. The Penultimate 6. Machine Learning
Lecture
1. Welcome 2. Combinatorics 3. Probability 4. Conditioning and Bayes 5. Independence 6. Conditional Independence and Random Variables 7. Variance, Bernoulli, and Binomial RVs 8. Poisson, Geometric, and Negative Binomial RVs 9. Continuous 10. Gaussian 11. Probabilistic Models 12. Independent Random Variables 13. Joint Statistics 14. Conditional Random Variables 15. Continuous Probabilistic Models 16. Continuous Inference 17. General Inference and Bayesian Networks 18. Central Limit Theorem 19. Sample Statistics and Bootstrapping 20. Beta 21. MLE: Maximum Likelihood Estimation 22. MAP: Maximum A Posteriori 23. Naive Bayes 24. Logistic Regression 25. Deep Learning 26. Ethics Of Machine Learning 27. Future Of Probability
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8 Section 9
Resources
Course Reader Draft Calculation Reference Probability Reference (overleaf) Probability in Python Latex in CS109 Latex Cheat Sheet Challenge
Galton Board Gaussian Calculator ELO Ratings Mini-WebMD Sampling Distributions Central Limit Theorem MLE Uniform Handwritten Digit Classification DRE and Atari-Like Games
Quizzes
Quizzes Overview Quiz 1 Quiz 2 Quiz 3

Schedule

Section 9

Mar 16th, 2021

This week's section operates as a review for Quiz 3.

Section Materials

Section Handout

Find Meeting Link

Section Recording