CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos Language Model Tool Handout
Challenge Midterm
Problem Sets
1. Core Probability 2. Discrete Random Variables 3. Continuous Random Variables 4. Probabilistic Models 5. Uncertainty Theory 6. Machine Learning
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 17. Algorithm Analysis 18. Information Theory 19. KL Divergence 20. Maximum Likelihood Estimation 21. Logistic Regression 22. Comparing Classifiers
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7
Section Reassign Form
PEP
Midterm

Schedule

Lecture 25: Deep Learning

Mar 10th, 2025
Hewlett 200, 3p

Lecture Materials

Learning Goals

Learn about feed forward deep learning networks and the theory behind back propagation.