UC Berkeley Spring 2017 Course
Introduction to Machine Learning
For the Spring 2017 semester, I am an Undergraduate Student Instructor. Also see resources that I produced Fall 2016.
These are designed to be streamlined, covering only quintessential proofs that provide insight. As of current, they are structured to match Professor Shewchuk's lecture notes.
- Note 1 : Introduction & Abstractions
- Note 2 : Perceptrons
- Note 3 : Support Vector Machines
- Note 4 : Convex Optimization
- Note 5 : Decision Theory
- Note 6 : Gaussian Discriminant Analysis (LDA, QDA)
- Note 7 : Decompositions (EVD, SVD)
These quizzes are not for a grade. However, it is still in your best interest to complete them.
- 1/31 Perceptrons Quiz 02 (Solutions)
- 2/7 Support Vector Machines, Convex Optimization Quiz 03 (Solutions)
- 2/14 Gaussian Discriminant Analysis, Decompositions Quiz 04 (Solutions)
Crib sheets contain cheat-sheet worthy material. They are not substitutes for lecture or for readings.
- 2/14 Gaussian Discriminant Analysis, Decompositions Crib 04
Extra resources and documents that I've written.