CS 109 Notes
Note: credit for most figures and lots of paragraphs goes to CS 109.
| Date | Title | Reading Time |
|---|---|---|
| Lecture 11 - Probablistic models | 5 min | |
| Lecture 12: Inference part I | 2 min | |
| Lecture 13: Inference part II | 2 min | |
| Lecture 14 - Modeling | 4 min | |
| Lecture 15: General Inference | 3 min | |
| Lecture 16 - Beta | 10 min | |
| Lecture 17: Adding Random Variables | 4 min | |
| Lecture 18: Central Limit Theorem | 1 min | |
| Lecture 19: Bootstrapping | 7 min | |
| Lecture 20: Algorithmic Analysis | 2 min | |
| Lecture 24: Logistic Regression | 4 min | |
| Lecture 25: Ethics in Machine Learning | 0 min | |
| Lecture 26: Deep Learning | 0 min | |
| Reference | 5 min | |
| 🌋 Lecture 21: Maximum Likelihood Estimation | 5 min | |
| 🐻 Lecture 23: Naïve Bayes | 0 min | |
| 🛶 Lecture 22: Maximum A Posteriori | 2 min |
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