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

### 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.

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