Public Course Guides + New Course Guide

Berkeley CS281a: Statistical Learning Theory
Created by: Colorado Reed
Intended for: students taking CS281a at Berkeley
This roadmap outlines the concepts discussed in CS281a.

Coursera: Machine Learning
Created by: Colorado Reed
Intended for: Coursera Machine Learning Students
A supplement to Andrew Ng's Coursera machine learning course

MIT 6.438: Algorithms for Inference
Created by: Roger Grosse
Intended for: MIT 6.438 students
An overview of the topics covered in 6.438, MIT's probabilistic graphical models course

Stanford CS106B: Programming Abstractions
Created by: Roger Grosse
Intended for: CS106B students, those new to programming
CS106B is the second course in Stanford's introductory programming sequence. It covers some basic data structures (trees, hash tables, etc.) and looks at how they can be implemented in C++.

Stanford CS229: Machine Learning
Created by: Roger Grosse
Intended for: CS229 students, anyone interested in machine learning
CS229 is Stanford's graduate course in machine learning, currently taught by Andrew Ng. It provides an overview of techniques for supervised, unsupervised, and reinforcement learning, as well as some results from computational learning theory.

Stanford Phil 151: FirstOrder Logic
Created by: Roger Grosse
Intended for: Phil 151 students, anyone interested in logic
Phil 151 is the second term in Stanford's undergraduate logic sequence. It formally investigates the syntax and semantics of firstorder logic, culminating in a proof of Godel's Completeness Theorem and a discussion of some of its consequences.