Fall 2016 Artificial Intelligence (CS440/ECE448 Sections Q3, Q4, ONL)

Quick links: announcements (updated 11/30), 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, 3:30PM-4:45PM, 1404 Siebel

Instructor: Svetlana Lazebnik (slazebni -at- illinois.edu)
Office hours (3308 Siebel): TBA Tuesdays and Thursdays 1:30-3PM or by appointment.

TAs: Hyo Jin Do (hjdo2), Manav Kedia (mkedia2), Shreya Rajpal (srajpal2), Xuesong Yang (xyang45), Aditi Adhikari (adhikar).
TA office hours (207 Siebel): Mondays 5PM-7PM, Wednesdays 5PM-7PM, Fridays 4PM-7PM

Contacting the course staff: For emergencies and special circumstances (including extension requests), please email the instructor. For questions about lectures and assignments, use Piazza. For questions about your scores (including regrade requests), email the responsible TAs.

Always check announcements and Piazza 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
    August 23 Intro to AI: PPT, PDF Reading: Ch. 1
    August 25 History and themes: PPT, PDF Reading: Ch. 1
    August 30 Agents: PPT, PDF Reading: Ch. 2
    September 1 Search intro: PPT, PDF Reading: Ch. 3
    September 6 Uninformed search: PPT, PDF Reading: Sec. 3.1-3.4
    September 8 Informed search: PPT, PDF Reading: Sec. 3.5-3.6
    Homework: Assignment 1 is out
    September 13 Constraint satisfaction problems: PPT, PDF Reading: Ch. 6
    September 15 CSPs cont. (slides above)  
    September 20 Minimax search: PPT, PDF Reading: Ch. 5
    September 22 Stochastic tree search and stochastic games: PPT, PDF Assignment 1 due September 26 11:59:59PM
    September 27 Game theory: PPT, PDF Reading: Sec. 17.5-17.6
    September 29 Game theory cont. Homework: Assignment 2 is out
    October 4 Planning: PPT, PDF Reading: Ch. 10
    October 6 Probability: PPT, PDF Reading: Ch. 13
    October 11 Midterm review: PDF  
    October 13 Midterm (in class)  
    October 18 Probability cont. Reading: Ch. 13
    October 20 Bayesian inference: PPT, PDF Assignment 2 due October 24 11:59:59PM
    October 25 Bayesian networks: PPT, PDF Reading: Ch. 14
    October 27 Bayesian networks cont. Homework: Assignment 3 is out
    November 1 Bayesian network inference: PPT, PDF Reading: Ch. 20
    November 3 Hidden Markov models: PPT, PDF Reading: Ch. 15, sec. 23.5
    November 8 Markov decision processes: PPT, PDF Reading: Ch. 17
    November 10 Reinforcement learning: PPT, PDF Reading: Ch. 21
    Assignment 3 due November 14
    November 15 Machine learning: PPT, PDF Homework: Assignment 4 is out
    November 17 Perceptrons, neural networks: PPT, PDF  
    November 29 Class cancelled  
    December 1 Deep learning: PPT, PDF Assignment 4 due December 5
    December 6 Final review: PDF  
    December 12 Final exam: 9-10:15AM, 1404 Siebel