COMP 590 Artificial Intelligence



Fall 2011, Tuesdays and Thursdays, 3:30-4:45 PM

Instructor: Svetlana Lazebnik (lazebnik -at- cs.unc.edu)

Credit: 3 units

Prerequisites: basic programming and data structures (COMP 401 and 410), algorithms (COMP 550) highly desired, basic calculus, familiarity with probability concepts a plus but not required.

Textbook: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd or 3rd edition.

Description: 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 maximally 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 logic to probabilistic reasoning, and a broad range of applications, from natural language understanding to robotics. Lectures will stress not only the technical concepts themselves, but also the history of ideas behind them.

Grading:

Tentative syllabus

Schedule

Date Topic Readings and assignments
August 23 Intro to AI: PPT, PDF Reading: Ch. 1
August 25 History of AI: 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 and informed search: PPT, PDF Reading: Sec. 4.1-4.2 (2nd ed.), Ch. 3 (3rd ed.)
September 8 Local search: PPT, PDF Reading: Sec. 4.3 (2nd ed.), Sec. 4.1 (3rd ed.)
Homework: Assignment 1
September 13 Constraint satisfaction problems: PPT, PDF Reading: Ch. 5 (2nd ed.), Ch. 6 (3rd ed.)
September 15 CSPs concluded; intro. to games  
September 20 Adversarial search: PPT, PDF Reading: Ch. 6 (2nd ed.), Ch. 5 (3rd ed.)
September 22 Game theory: PPT, PDF Reading: Sec. 17.6-17.7 (2nd ed.), 17.5-17.6 (3rd ed.)
September 27 Game theory cont.: PPT, PDF Assignment 1 due at 11:59PM
Homework: Assignment 2
September 29 Propositional logic: PPT, PDF Reading: Ch. 7
October 4 Guest lecture (Joe Tighe): computer vision: PPT Reading: Ch. 24
October 6 Guest lecture (Joe Tighe): image parsing: PPT  
October 11 First-order logic: PPT, PDF Reading: Ch. 8
Practice midterm questions:DOC, PDF
October 13 FOL inference: PPT, PDF
Midterm review
Reading: Ch. 9
October 18 Midterm  
October 25 Probability: PPT, PDF Reading: Ch. 13
October 27 Bayesian inference: PPT, PDF  
November 1 Bayesian inference cont. Assignment 2 due at 11:59PM
November 3 Bayesian networks: PPT, PDF Reading: Ch. 14
Homework: Assignment 3
November 8 Class cancelled  
November 10 Guest lecture: Ron Alterovitz  
November 15 Bayesian network inference: PPT, PDF Reading: Ch. 20
November 17 Markov decision processes: PPT, PDF Reading: Ch. 17
Assignment 3 due at 11:59PM
November 22 Reinforcement learning: PPT, PDF Reading: Ch. 21
Homework: Assignment 4
November 29 Machine learning: PPT, PDF Practice midterm 2 questions:DOC, PDF
December 1 Conclusion: PPT, PDF
Final review
 
December 6 Midterm 2 Assignment 4 and extra credit due December 10 at 11:59PM -- no extensions!