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: First-Order 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 first-order logic, culminating in a proof of Godel's Completeness Theorem and a discussion of some of its consequences.