CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos AIWG Student Guide
Challenge Midterm
Problem Sets
1. Core Probability 2. Discrete Random Variables 3. Continuous Random Variables 4. Probabilistic Models 5. Uncertainty Theory 6. Information Theory + MLE 7. Machine Learning
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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. Algorithm Analysis 17. Information Theory 18. Sampling & Bootstrapping 19. Maximum Likelihood Estimation 20. Logistic Regression 21. Comparing Classifiers 22. Deep Learning
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Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7
Section Reassign Form Times and Locations
PEP
Midterm Final

Schedule

Lecture 22: Deep Learning

March 2nd, 2026
Hewlett 200, 3p

Lecture Materials

Learning Goals

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