COMP 590 Artificial Intelligence

Quick links: syllabus, schedule

Fall 2010, Tuesdays and Thursdays, 3:30-4:45 PM

Instructor: Svetlana Lazebnik (lazebnik -at-
Teaching assistant: Danny Kumar (ndkumar -at-

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.


Tentative syllabus


Date Topic Readings* and assignments
*Chapter numbers refer to 3rd ed.
August 24 Intro to AI: PPT, PDF Reading: Ch. 1
August 26 History of AI: PPT, PDF Reading: Ch. 1
August 31 Agents: PPT, PDF Reading: Ch. 2
September 2 Search: PPT, PDF Reading: Ch. 3
Homework: Assignment 1 out
September 7 Guest lecture: Jur van den Berg: PPT  
September 9 Guest lecture: Ron Alterovitz  
September 14 Uninformed and informed search: PPT, PDF Reading: Ch. 3
Assignment 1 due at 3PM
September 16 Local search: PPT, PDF Reading: Ch. 4
September 21 Constraint satisfaction problems: PPT, PDF Reading: Ch. 6
Homework: Assignment 2 out
September 23 CSPs cont.: PPT, PDF  
September 28 Adversarial search: PPT, PDF Reading: Ch. 5
September 30 Game theory: PPT, PDF Reading: Sec. 17.5, 17.6
October 5 Game theory cont.: PPT, PDF Reading: Ch. 7
October 7 Propositional logic: PPT, PDF Assignment 2 due at 11:59 PM
October 12 First-order logic: PPT, PDF Reading: Ch. 8, 9
October 14 Midterm review: DOC, PDF  
October 19 Midterm  
October 26 Logic concluded: PPT, PDF Homework: Assignment 3 out
October 28 Probability: PPT, PDF Reading: Ch. 13
November 2 Bayesian inference: PPT, PDF Reading: Ch. 13
November 4 Bayesian inference concluded: PPT, PDF  
November 9 Bayesian networks: PPT, PDF Reading: Ch. 14
November 11 Bayesian network inference: PPT, PDF Reading: Ch. 14
November 16 Hidden Markov models: PPT, PDF
(guest lecturer: Danny Kumar)
Reading: Ch. 15
November 18 HMM inference: PPT, PDF Assignment 3 due on November 22nd at 11:59 PM
November 23 Speech: PPT, PDF
Markov decision processes: PPT, PDF
Reading: Sec. 23.5, Ch. 17
Homework: Assignment 4 out
November 30 Reinforcement learning: PPT, PDF Reading: Ch. 21
December 2 Classification: PPT, PDF Reading: Ch. 18
December 7 Course wrap-up
Final review: DOC, PDF
December 17 4PM Final Assignment 4 due on December 14th at 11:59 PM