AI University

AI University. [originally published here] Many of the links come from Video Lectures. The thesis is that the courses available online can form a solid education in AI. I have updated the list to provide a more balanced program, aiming at "university replacement". Tentatively one could go through four courses in a semester. I will add links to textbooks later.

  1. year:
    1. Introduction to Logic by Michael Genesereth
      1. General Game Playing by Michael Genesereth, also Michael Thielscher and Sam Schreiber -- at this point, take a quick tour by watching the (short and simple) lectures
    2. Probability Primer by mathematicalmonk
    3. Calculus: Single Variable by Robert Ghrist
    4. Algorithms, either one of:
      1. Algorithms: Design and Analysis, Part 2 by Tim Roughgarden
      2. Algorithms, Part 2 by Kevin Wayne and Robert Sedgewick
      3. Introduction to Algorithms by Charles Leiserson and Erik Demaine
    5. Functional Programming Principles in Scala by Martin Odersky
    6. Machine Learning  by Andrew Ng
    7. Introduction to Cognitive Architectures seminar:
      1. Cognitive Architectures by Włodek Duch
      2. Clarion Tutorial, Clarion Part 2 by Michael Lynch
      3. The Soar Cognitive Architecture  by Nate Debrinsky
      4. OpenCog by Ben Goertzel
      5. From Constructionist to Constructivist A.I. by Kristinn R. Thórisson
      6. Deconstructing Reinforcement Learning in Sigma, Modeling Two-Player Games in the Sigma Graphical Cognitive Architecture by Paul Rosenbloom
      7. Pursuing Artificial General Intelligence By Leveraging the Knowledge Capabilities Of ACT-R by Alesandro Oltramari
      8. A Cognitive Architecture based on Dual Process Theory (perception vs. imagination) by Claes Strannegård
    8. Scientific Approaches to Consciousness  by John F. Kihlstrom
  2. year:
    1. Probabilistic Graphical Models by Daphne Koller
    2. Course on Information Theory, Pattern Recognition, and Neural Networks by David MacKay
    3. Introduction to Modal Logic by Rajeev P. Goré
    4. Introduction to Databases  by Jennifer Widom
    5. Learning From Data  by Yaser Abu-Mostafa (Machine Learning with elements of Statistical Learning Theory)
    6. Linear Algebra by Gilbert Strang, also:
      1. Complex Analysis by Petra Bonfert-Taylor (optional)
      2. Differential Equations by Arthur Mattuck (optional)
      3. Introduction to Functional Analysis  by Richard Melrose (optional)
      4. Nonlinear Dynamics I: Chaos by Daniel Rothman (optional)
      5. Differential Geometry  by Paul Seidel (optional)
      • The optional math classes are meant to be picked up later as your time allows. You should at least have basic familiarity with: complex numbers; calculus and differential equations; linear operators: matrix representation in various bases, nullspaces, orthogonal complement.
    7. For a round number of courses, pick one more of the math courses above
    8. Introduction to Philosophy  by Richard Brown
  3. year:
    1. Discrete Optimization by Pascal Van Hentenryck
    2. Artificial Intelligence Planning by Gerhard Wickler and Austin Tate
    3. Introduction to Formal Languages, Automata and Computational Complexity  by Jeff Ullman
    4. Natural Language Processing, one of, or both:
      1. Dan Jurafsky and Christopher Manning
      2. Michael Collins
    5. Neural Networks
      1. Neural Networks by Geoffrey Hinton
      2. Neural Networks class by Hugo Larochelle
      3. Graphical Models and Variational Methods by Christopher Bishop
    6.  Either:
      1. Machine Learning (review and continuation) by Andrew Ng, or
      2. Introduction to Machine Learning by Alex Smola.
      • Skip over parts that you are confident to know already.
    7. Linear Dynamical Systems by Stephen Boyd
    8. Computational Neuroscience by Rajesh P. N. Rao and Adrienne Fairhall
  4. year:
    1. Game Theory  by Kevin Leyton-Brown, Matthew O. Jackson and Yoav Shoham (optional)
    2. General Game Playing by Michael Genesereth, also Michael Thielscher and Sam Schreiber -- at this point, treat it as a project course, build your own player using knowledge from other courses
    3. Convex Optimization by Stephen Boyd (optional)
    4. Reinforcement Learning -- sorry for redundancy with each other and with pieces in Andrew Ng, try to find your way
      1. by Csaba Szepesvári,
      2. by Satinder Singh Baveja,
      1. Foundations of Machine Learning by Marcus Hutter,
      2. Richard Sutton AGI 2010 Keynote Address, Part 2 
      3. GQ(lambda)- A General Gradient Algorithm for Temporal-Difference Prediction Learning with Eligibility Traces by Hamid Reza Maei
    5. Abstract Algebra  by Benedict Gross
    6. Overview of Automated Reasoning  by Peter Baumgartner
    7. Type Theory Foundations and Proof Theory Foundations by Robert Harper and Frank Pfenning respectively
    8. Ethics and Moral Issues  by Richard Brown
  5. year:
    1. Big Data, Large Scale Machine Learning  by John Langford and Yann LeCun
    2. Graduate Summer School: Deep Learning, Feature Learning at UCLA
    3. Statistical Learning Theory by John Shawe-Taylor and by Olivier Bousquet / newer variant of Olivier's
    4. Practical Statistical Relational Learning by Pedro Domingos
    5. Online Learning, Regret Minimization, and Game Theory by Avrim Blum
    6. Introduction to Category Theory by error792
    7. Computer Vision by Mubarak Shah
    8. Cognitive Architectures and Modeling Course -- perhaps some combination of these, but there is no good course online:
      1. Representations: Classes, Trajectories, Transitions and Architectures: GPS, SOAR, Subsumption, Society of Mind by Patrick H. Winston, as introduction
      2. Cognitive Science and Machine Learning Summer School videos
      3. Cognitive Modeling by John Anderson and T.A. Phil Pavlik
        1. Cognitive Modelling by Sharon Goldwater
      4. AGI 2011 ArchitecturesPart 2 and other AGI Conference presentation videos