Fall 2015 Artificial Intelligence (CS440/ECE448)

Quick links: announcements (updated 12/9), 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): Tuesdays and Thursdays 1:30-3PM or by appointment.

TAs: Codruta Girlea (girlea2 -at- illinois.edu),
Qieyun Dai (dai9 -at- illinois.edu),
Huan Gui (huangui2 -at- illinois.edu),
Yiming Jiang (yjiang16 -at- illinois.edu),
Keyang Zhang (kzhang53 -at- illinois.edu).
TA office hours (207 Siebel): Mondays 5-7PM, Wednesdays 10-11AM, Fridays 2-4PM.

Email for contacting course staff: cs440-fall2015 -at- googlegroups.com

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:

  Be sure to read the course policies!

Syllabus (tentative)

Announcements

Schedule (tentative)

Date Topic Readings and assignments
August 25 Intro to AI: PPT, PDF Reading: Ch. 1
August 27 History and themes: PPT, PDF Reading: Ch. 1
September 1 Agents: PPT, PDF Reading: Ch. 2
September 3 Search intro: PPT, PDF Reading: Ch. 3
September 8 Uninformed search: PPT, PDF Reading: Sec. 3.1-3.4
September 10 Informed search: PPT, PDF Reading: Sec. 3.5-3.6
Homework: Assignment 1 is out
September 15 Constraint satisfaction problems: PPT, PDF Reading: Ch. 6
September 17 CSPs cont. (slides above)  
September 22 Adversarial search: PPT, PDF Reading: Ch. 5
September 24 Adversarial search cont. (slides above) Assignment 1 due September 28 11:59:59PM
September 29 Game theory: PPT, PDF Reading: Sec. 17.5-17.6
October 1 Game theory cont. (slides above) Homework: Assignment 2 is out
October 6 Planning: PPT, PDF Reading: Ch. 10
October 8 Probability: PPT, PDF Reading: Ch. 13
October 13 Midterm review: PDF (see Compass for solutions)  
October 15 Midterm (in class)  
October 20 Probability (slides from 10/8) Reading: Ch. 13
October 22 Bayesian inference: PPT, PDF Assignment 2 due October 26 11:59:59PM
October 27 Bayesian inference cont. (slides above) Homework: Assignment 3 is out
October 29 Bayesian networks: PPT, PDF Reading: Ch. 14
November 3 Bayesian network inference: PPT, PDF Reading: Ch. 20
November 5 Sequential probabilistic reasoning: PPT, PDF Reading: Ch. 15
November 10 Markov decision processes: PPT, PDF Reading: Ch. 17
November 12 Reinforcement learning: PPT, PDF Reading: Ch. 21
Assignment 3 due November 16 11:59:59PM
November 17 Lecture cancelled Homework: Assignment 4 is out
November 19 Machine learning: PPT, PDF  
December 1 Neural networks and support vector machines: PPT, PDF  
December 3 Deep learning: PPT, PDF Assignment 4 due December 7 11:59:59PM
December 8 Final review: PDF (see Compass for solutions)  
December 15 Final exam, 7-8:15PM, 1404 Siebel