## Machine Learning

31 Aug 2021

Machine learning is a deep and broad enough field that it could be its own college major. If I was designing a machine learning major, here are the core classes I would include. (This can also serve as a personal study guide.)

For some topics, I’ve linked to a set of class notes or textbook that I’ve used and enjoyed. I favor class notes wherever possible.

Let me know if I’m missing something! (or if you just love any of these topics!)

### Core

Foundations

Reinforcement Learning

^{1}

Theory

Statistical Learning Theory

^{2}Probabilistic Graphical Models

^{3}

Systems

Applications

Computer Vision

^{4}Natural Language Processing

^{5}Speech Recognition

Robotics

Time Series Forecasting

### Background

Programming

- Python (with numpy)

Mathematics

Probability & Statistics

Multivariable Calculus

### Enrichment

Mathematics

Optimization

Information Theory

Biology

Neuroscience

Cognitive Psychology

^{6}

Electrical Engineering

- Signal Processing

### Footnotes

Class notes I haven’t used, but that I’d recommend:

1: Check out Stanford’s CS 234 video lectures or David Silver (Deep Mind)’s course.

2: Check out Stanford’s STATS 214 class page for references to some good SLT resources. Percy Liang’s notes, in particular, look good.

3: Check out Stefano Ermon’s (rather concise) notes.

4: Check out Stanford’s CS 131 syllabus and lecture notes.

5: Check out Stanford’s CS 224N syllabus and video lectures.

6: I can’t miss an opportunity to shill Steven Pinker. Check out his PSY 101 videos.

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