Samvit Jain's blog

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

• Artificial Intelligence

• Statistical Learning

• Deep Learning

• Reinforcement Learning ¹

Theory

• Statistical Learning Theory ²

• Probabilistic Graphical Models ³

Systems

• Computer Architecture

• Distributed Systems

Applications

• Computer Vision ⁴

• Natural Language Processing ⁵

• Speech Recognition

• Robotics

• Time Series Forecasting

Background

Programming

• Python (with numpy)

Mathematics

• Probability & Statistics

• Linear Algebra

• 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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