CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos AIWG Student Guide
Midterm 1
Problem Sets
1. Core Probability 2. Discrete Random Variables 3. Continuous Random Variables 4. Probabilistic Models
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 16. Central Limit Theorem

Schedule

Lecture 23: Deep Learning

May 22, 2026
CoDa B80, 10:30am

Lecture Materials

Learning Goals

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