Fall 2017 Artificial Intelligence (CS440/ECE448 Sections Q3, Q4, ONL)

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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): Tuesdays and Thursdays 1:30-3PM or by appointment.

TAs: Akshat Gupta (agupta60), Kyo Hyun Kim (kkim103), Krishna Kothapalli (kk20), Litian Ma (litianm2), Andrey Zaytsev (zaytsev2)
TA office hours (207 Siebel): Mondays 3-5PM, Wednesdays 12-2PM, Thursdays 11AM-12PM and 6:20-7:20PM, Fridays 5-7PM.

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 on Piazza for short-notice changes to instructor and TA office hours!

Prerequisites: data structures (CS 225 or equivalent), algorithms highly desirable, basic calculus, intro to probability.

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 29 Intro to AI: PPT, PDF Reading: Ch. 1
    August 31 History and themes: PPT, PDF Reading: Ch. 1
    September 5 Agents: PPT, PDF Reading: Ch. 2
    September 7 Search intro: PPT, PDF Reading: Ch. 3
    September 12 Uninformed search: PPT, PDF Reading: Sec. 3.1-3.4
    September 14 Informed search: PPT, PDF Reading: Sec. 3.5-3.6
    Homework: Assignment 1 is out
    September 19 Constraint satisfaction problems: PPT, PDF Reading: Ch. 6
    September 21 CSPs cont. (slides above)  
    September 26 Planning: PPT, PDF Reading: Ch. 10
    September 28 Minimax search: PPT, PDF Reading: Sec. 5.1-5.4
    Assignment 1 due October 2
    October 3 Stochastic tree search and stochastic games: PPT, PDF Reading: Sec. 5.5-5.6
    October 5 Game theory: PPT, PDF Reading: Sec. 17.5-17.6
    Homework: Assignment 2 is out
    October 10 Game theory cont. (slides above)  
    October 12 Midterm review: PDF (solutions on Compass)  
    October 17 Midterm (in class)  
    October 19 Probability: PPT, PDF Reading: Ch. 13
    October 24 Bayesian inference: PPT, PDF Reading: Ch. 13
    October 26 Bayesian networks: PPT, PDF Reading: Ch. 14
    Assignment 2 due October 30
    October 31 Bayesian networks cont. Reading: Ch. 14
    November 2 Bayesian network inference: PPT, PDF Reading: Ch. 20
    Homework: Assignment 3 is out
    November 7 Machine learning: PPT, PDF Reading: Ch. 18
    November 9 Support vector machines: PPT, PDF Reading: Sec. 18.6, 18.9
    November 14 Neural networks: PPT, PDF Reading: Sec. 18.7
    November 16 Deep learning: PPT, PDF Assignment 3 due November 27
    November 28 Markov decision processes Reading: Ch. 17
    Homework: Assignment 4 is out
    November 30 Reinforcement learning Reading: Ch. 21
    December 5 Reinforcement learning cont.  
    December 7 Societal impacts of AI Assignment 4 due December 11
    December 12 Final review  
    December 18 Final exam: 1:30-2:45PM, room TBA