• Artificial Intelligence (General)
    • List of IntroAI courses (Russell)
    • Artificial Intelligence: Principles and Techniques (Koller: Stanford [CS 221: Autumn 99])
    • Fundamentals of Artificial Intelligence (Moore: CMU [15-381: Fall 1999])
    • Artificial Intelligence Programming Techniques (Dietterich: OregonSt [CS430/530])
    • Introduction to AI (Schuurmans: UofWaterloo [486])
    • Introduction to Artificial Intelligence (Poupart: UofToronto [CSC384S], Spring 2003)
    • Foundations of Artificial Intelligence (Selman: Cornell [CS472/473])
    • Principles of Artificial Intelligence (Elkan: UCSD [CSE 250A])
    • Lecture notes (David Poole: UBC)
  • Machine Learning
    • N Ray, "Cmput466/551 W09" (UofA, Winter 2009)
    • C. Guestrin, "Machine Learning" (CMU, Fall 2007)
    • R. Parr, "Machine Learning" (Duke, Fall 2007)
    • Bar-Joseph/Moore "Machine Learning" (CMU, Fall 2004)
    • List of Machine Learning courses
    • Data Mining Course (Piatetsky-Shapiro, Parker, 2004)
    • Machine Learning (Shavlik: UofWisconsin [CS760: 2000])
    • Adpative System (Subramanian: Rice [Comp540, Spring 2000])
    • Machine Learning (Dietterich: Oregon St [CS534])
    • Machine Learning (Mitchell: CMU [15:681, 15:781], Fall 98)
    • Machine Learning (Koller: Stanford [CS 229: Spring 99])
    • Learning (Dartmouth)
    • Machine Learning (Shavlik: UWisc [CS760])
    • Learning Overview (Temple University)
    • Machine Learning (Mahadevan: Univ of Southern Florida [6390])
    • Intro Slides (Aha: Navy)
    • Theoretical
      • Machine Learning (Rivest: MIT [6.858/18.428])
      • Machine Learning Theory (Blum: CMU [15:854], 98)
      • Machine Learning (Cohen, Freund, Schapire: Rutgers [536], 99)
      • Topics in AI: Computational Learning (Schuurmans: UofWaterloo [786])
      • Computational Learning Theory (Scott: WUSTL)
    • Topics
      • Neural Networks (Pearlmutter: UNM [ECE547 / CS591.04])
      • Machine Learning & Language Modeling (Michael Brent: JHU)
      • Reinforcement Learning: A Tutorial (Harmon)
      • Learning Dynamical Systems: A Tutorial (Brown, 1995)
  • Uncertainty Management
    • Reasoning with Partial Beliefs (Darwiche: UCLA [CS 262A, Winter 2001])
    • Advanced AI concepts: Decisions, Diagnosis, Cooperation, Learning and Planning in uncertain worlds (Moore: CMU [CS, Spring 2000])
    • Probabilistic Agents (Dietterich: Oregon State [CS539, Winter 00])
    • Artificial Intelligence: Knowledge Representation and Reasoning under Uncertainty (Koller: Stanford [CS 228, Winter 99])
    • Probabilistic Methods in AI (Friedman: Hebrew University [67800, Fall 99])
    • Decision Making Under Uncertainty (Boutilier: UofToronto [CSC2534, Winter 00]
    • Elements of Uncertain Reasoning (Tirri: University of Helsinki [5811267-5])
    • Mark Peot's BN course
    • Tom Richardson's BN course
    • Mike Kearn's course
  • BioInformatics
    • Bioinformatics (Craven: UofWisc [CS 838 - Fall 1999])
    • Representations and Algorithms for Computational Molecular Biology (Altman: Stanford [CS 274 - Spring 2000])
  • Other
    • Empirical Methods for Computer Science (Cohen, UMass [CS 691a]
    • Frey's Tutorials