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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)
TAs: Codruta Girlea (girlea2 -at- illinois.edu), |
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:
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 |