UC Berkeley Fall 2016 Course
Introduction to Machine Learning
For the Fall 2016 semester, I was just a student. I took notes for each lecture and released them below.
Lecture Notes
- 9/22 Note 9 : Gaussian Discriminant Analyses (LDA, QDA)
- 9/27 Note 10 : Linear Regression
- 9/29 Note 11 : Logistic Regression
- 10/4 Note 12 : Bias-Variance Tradeoff
- 10/6 Note 13 : Regularization
- 10/11 Note 14 : Cross Validation
- 10/13 Note 15 : Fairness in Machine Learning
- 10/20 Note 16 : Neural Networks : Derivation
- 10/25 Note 17 : Neural Networks : Inspiration in Biology
- 10/27 Note 18 : Convolutional Neural Networks
- 11/1 Note 19 : Kernels
- 11/3 Note 20 : Nearest Neighbors
- 11/8 Note 21 : Randomized Decision Trees
- 11/10 Note 22 : Randomized Decision Trees II
- 11/15 Note 23 : Singular Value Decomposition
- 11/18 Note 24 : Principal Component Analysis
- 11/22 Note 25 : Clustering
- 11/29 Note 26 : Clustering with Gradient Descent
- 12/1 Note 27 : Closing Remarks
Crib Sheets
Crib sheets contain cheat-sheet worthy material. They are not substitutes for lecture or for readings.
Extras
Extra resources and documents that I've written.