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

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:

• Programming assignments or MPs (mini-projects): 60%.
  • MPs can be done in groups of up to three people. Three-unit students must work with three-unit students and four-unit students must work with four-unit students.
  • Four-unit students will have to solve extra problems on every assignment.
• Midterm and final: 40%.
  • Short answer format, closed book, closed notes, no electronic devices.
• Extra credit:
  • Three-unit students can get extra credit for doing four-unit problems (the raw point value will be discounted by 50%).
  • For four-unit students who drop down to three units, the points earned on four-unit problems will convert to extra credit (with a discount of 50%).
  • A bonus of up to 3% of total course grade will also be given for active participation.

For details, see the grading scheme and statistics from a previous semester.

  Be sure to read the course policies!

Syllabus (tentative)

• AI: Concepts and history
• Solving problems by searching
  • Uninformed search
  • Informed search
  • Constraint satisfaction problems
• Games
  • The minimax principle
  • Modern game-playing systems
  • Game theory
• Planning
• Probabilistic reasoning
  • Basic probability concepts
  • Bayesian inference
  • Naive Bayes models
  • Bayesian networks
• Machine learning
  • Supervised vs. unsupervised learning
  • Decision trees
  • Nearest neighbor classifiers
  • Neural networks
  • Deep learning
• Reinforcement learning
  • Markov decision processes
  • Q-learning
  • Deep reinforcement learning
• Applications of AI
  • Robotics
  • Computer vision
  • Natural language processing
• AI, ethics, and society

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: PPT, PDF	Reading: Ch. 17 Homework: Assignment 4 is out
November 30	Reinforcement learning: PPT, PDF	Reading: Ch. 21
December 5	Deep reinforcement learning: PPT, PDF
December 7	Societal impacts of AI: PPT, PDF	Assignment 4 due December 11
December 12	Final review: PDF
December 18	Final exam: 1:30-2:45PM 180 Bevier Hall (last names starting with A-L) 103 Mumford Hall(last names starting with M-Z)