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CS486/686 Spring 2023 - Introduction to Artificial Intelligence

This is a tentative schedule only. As the course progresses, the schedule will be adjusted.

  • [RN3] 3rd edition of Artificial Intelligence: A Modern Approach by Russell and Norvig
  • [P] 2nd edition of Causality: Models, Reasoning and Inference by Pearl
  • [ZLLS] Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola, Dive into Deep Learning (forthcoming)
  • [SutBar] 2nd edition of Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
  • [GBC] 1st edition of Deep Learning by Ian Goodfellow, Yoshua Bengia and Aaron Courville
    • Lecture Date Topic Readings (textbooks)
    1 May 9 Introduction to Artificial Intelligence (slides) [RN3] Chapt. 1 and 2
    2 May 11 Uninformed Search (slides) [RN3] Sect. 3.1-3.4
    3 May 16 Informed Search (slides, annotated slides) [RN3] Sec. 3.5, 3.6
    4 May 18 Constraint Satisfaction (slides) [RN3] Sec 6.1-6.3
    5 May 25 Uncertainty (slides, annotated slides) [RN3] Sect. 13.1-13.5
    May 26 Assignment 1 due (11:59 pm)
    6 May 30 Bayesian Networks (slides) [RN3] Sections 14.1, 14.2, 14.4
    7 June 1 Bayesian Networks (continuation of the last lecture) [RN3] Sections 14.1, 14.2, 14.4
    8 June 6 Causal Inference (slides, annotated slides) [P] Chapter 1
    9 June 8 Probabilistic Reasoning over Time (slides) [RN3] Sec 15.1-15.3, 15.5
    June 9 Assignment 2 due (11:59 pm)
    10 June 13 Decision Tree Learning (slides, annotated slides) [RN3] Sec 18.1-18.4
    11 June 15 Statistical Learning (slides, annotated slides) [RN3] Sec 20.1-20.2
    12 June 20 Case Studies (slides) Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Tung and Allan Caine (2010) Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker, UAI.
    Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart (2023) NTS-NOTEARS: Learning Non-parametric DBNs with Prior Knowledge, AISTATS.
    13 June 22 Neural Networks (slides, annotated slides) [RN3] Sec 18.7, [ZLLS] Chapter 5
    June 23 Midterm (4:30 - 5:50 pm in M3-1006 and STC-0060). See seating assignment.
    14 June 27 Deep Neural Networks (slides, annotated slides) [ZLLS] Chapter 5
    15 June 29 Decision Networks (slides) [RN3] Sections 16.1 - 16.6
    June 30 Project proposal due at 11:59 pm (CS686 only)
    16 July 4 Markov Decision Processes (slides) [RN3] Sections 17.1 - 17.4, [SutBar] Chapters 3,4
    17 July 6 Reinforcement Learning (slides) [RN3] Sections 21.1 - 21.3, [SutBar] Chapters 5.1 - 5.3, 6.1 - 6.5
    July 7 Assignment 3 due (11:59 pm)
    18 July 11 Deep Reinforcement Learning (slides , annotated slides) [GBC] Chap. 6,7,8, [SutBar] Sec. 9.4, 9.7
    19 July 13 Model-based Reinforcement Learning (slides) [SutBar] Chap. 8
    20 July 18 Multi-armed Bandits (slides) [SutBar] Chap. 2
    21 July 20 Game Theory (slides) [RN3] Sections 17.5
    July 21 Assignment 4 due (11:59 pm)
    22 July 25 Game Theory II (slides, annotated slides) [RN3] Sections 17.5
    23 July 27 Multi-agent RL (slides, annotated slides) Caroline Claus and Craig Boutilier (1998)The Dynamics of Reinforcement Learning in Cooperative Multiagent Systems, AAAI.
    Michael Littman (1994) Markov games as a framework for multi-agent reinforcement learning, Machine learning proceedings.
    Junling Hu and Michael P. Wellman (2003) Nash Q-learning for General-Sum Stochastic Games, JMLR
    24 August 1 Multi-agent RL (continuation of the previous lecture)
    August 16 Project report due at 11:59 pm (CS686 only)