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