Fall 2016 Artificial Intelligence (CS440/ECE448 Sections R3, R4)

Quick links: announcements (updated 8/16), schedule, Compass2g (assignment submission, grades), lecture videos, Piazza (discussion board), course policies

The goal of Artificial Intelligence (AI) is the design of agents that can behave rationally in the real world by sensing their environment, planning their goals, and acting to optimally achieve these goals. This course provides an introductory survey to the techniques and applications of modern AI. The course will cover a broad range of conceptual approaches, from combinatorial search to probabilistic reasoning and machine learning, and a broad range of applications, from natural language understanding to computer vision. Lectures will stress not only the technical concepts themselves, but also the history of ideas behind them.

Lectures: Tuesdays and Thursdays, 2:00PM-3:15PM, 216 Siebel

Instructor: Mark Hasegawa-Johnson (jhasegaw -at- illinois.edu)
Office hours (2011 Beckman): Tuesdays and Thursdays 3:30-4:45PM or by appointment.


Always check announcements for short-notice changes to instructor and TA office hours!

Prerequisites: data structures (CS 225 or equivalent), algorithms highly desirable, basic calculus, familiarity with probability concepts a plus but not required.

Textbook: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd edition.

Grading scheme:

  • For details, see the grading scheme and statistics from a previous semester.

      Be sure to read the course policies!

    Syllabus (tentative)


    Schedule (tentative)

    Date Topic Readings and assignments Slides
    August 23 Intro to AI Reading: Ch. 1 lec01_intro
    August 25 History and themes Reading: Ch. 1 lec02_history
    August 30 Agents Reading: Ch. 2 lec03_agents
    September 1 Search intro Reading: Sec. 3.1-3.3 lec04_search_intro
    September 6 Uninformed search Reading: Sec. 3.4 lec05_uninformed_search
    September 8 Informed search Reading: Sec. 3.5-3.6
    Homework: Assignment 1 is out
    September 13 Lecture Cancelled
    September 15 Constraint satisfaction problems Reading: Ch. 6 lec07_csp
    September 20 Deterministic Games with Full Information Reading: Ch. 5 lec08_adversarial_search.pptx
    September 22 Policy Classifiers and Value Regressors; Stochastic Games Assignment 1 due September 26 lec09_stochastic_games.pptx
    September 27 Game theory Reading: Sec. 17.5-17.6 lec10_game_theory.pptx
    September 29 Game theory cont. Homework: Assignment 2 is out
    October 4 Planning Reading: Ch. 10 lec11_planning.pptx
    October 6 Probability Reading: Ch. 13 lec12_probability.pptx
    October 11 Midterm review review, solutions Exam 1 review
    October 13 Midterm (in class)  
    October 18 Bayesian inference Reading: Ch. 13 lec13_bayesian_inference.pptx
    October 20 Bayesian inference cont. Assignment 2 due October 24 lec13_bayesian_inference.pptx
    October 25 Bayesian Networks Homework: Assignment 3 is out lec14_bayes_nets.pptx
    October 27 Bayes Net Inference Reading: Ch. 20 lec15_bayes_net_inference.pptx
    November 1 Hidden Markov Models Reading: Ch. 15, 17 lec16_hmm.pptx
    November 3 Markov Decision Processes Reading: Ch. 17 lec17_mdp.pptx
    November 8 Reinforcement Learning Reading: Ch. 21 lec18_rl.pptx
    November 10 Machine Learning Reading: Ch. 21
    Assignment 3 due November 14
    November 15 Lecture Canceled Homework: Assignment 4 is out
    November 17 Lecture Canceled  
    November 29 Neural networks and support vector machines   lec20_nn_and_svm.pptx
    December 1 Deep learning Assignment 4 due December 5 lec21_cnn.pptx
    December 6 Final review   review problems.
    December 9, 14:00-15:15, Siebel 0216 Exam 2