CS 109: Probability for Computer Scientists

Stanford course with 25 lecture notes.

Lecture Timeline

• 2023-09-27 — SU-CS109 SEP272023
• 2023-09-29 — SU-CS109 SEP292023
• 2023-10-02 — SU-CS109 OCT022023
• 2023-10-04 — SU-CS109 OCT042023
• 2023-10-06 — SU-CS109 OCT062023
• 2023-10-09 — SU-CS109 OCT092023
• 2023-10-11 — SU-CS109 OCT112023
• 2023-10-16 — SU-CS109 OCT162023
• 2023-10-18 — SU-CS109 OCT182023
• 2023-10-20 — SU-CS109 OCT202023
• 2023-10-23 — SU-CS109 OCT232023
• 2023-10-25 — SU-CS109 OCT252023
• 2023-10-27 — SU-CS109 OCT272023
• 2023-11-01 — SU-CS109 NOV012023
• 2023-11-03 — SU-CS109 NOV032023
• 2023-11-06 — SU-CS109 NOV062023
• 2023-11-08 — SU-CS109 NOV082023
• 2023-11-10 — SU-CS109 NOV102023
• 2023-11-13 — SU-CS109 NOV132023
• 2023-11-15 — SU-CS109 NOV152023
• 2023-11-17 — SU-CS109 NOV172023
• 2023-11-27 — SU-CS109 NOV272023
• 2023-11-29 — SU-CS109 NOV292023
• 2023-12-01 — SU-CS109 DEC012023
• 2023-12-04 — SU-CS109 DEC042023

Key Topics Referenced

• Argmax
• Bayes Normalization Constant
• Bayes Theorem
• Bayes Theorem Over Random Variable
• Bernoulli Random Variable
• Binomial Distribution
• Boostrap
• Central Limit Theorem
• Combination
• Counting
• Deep Learning
• Demorgan S Law
• Double Envelope Problem
• Eigenvalue
• Event
• Expectation
• Exploration And Exploitation
• Exponential Distribution
• Function
• Galton Board
• General Inference
• Geometric Random Variable
• Grouping
• Independently And Identically Distributed
• Inference
• Item Response Theory
• Joint Probability Distribution
• Likelyhood
• Logistic Regression
• Machine Learning