Artificial Intelligence
Winter, 2017
Instructor: Jackie Chi Kit Cheung
Time: Tuesdays and Thursdays, 8:30am – 10:00am
Location: Bronfman 151
Office hours: Thursdays, 10:00am – 12:00pm, starting on January 12th
Office hours location: McConnell 108N
Course outline
TAs: Ali Emami, Christopher Glasz, Michael Noseworthy, Harsh Satija, Matthew Smith
This course presents an introduction to the the field of artificial intelligence. Topics covered include: Search methods, knowledge representation using logic and probability, planning and decision making under uncertainty. Introduction to machine learning.
Prerequisites: (COMP 206 or ECSE 321), MATH 323 or equivalent and COMP 251.
Announcements
Classes start January 5th. All further announcements will be made in-class and on myCourses.Lectures and Readings
Below is a tentative schedule of the topics in the course, and subject to modifications. Lectures slides and recordings will be made available on myCourses.
Date | Topic | Readings |
---|---|---|
Jan 5 | Introduction to AI | R&N Ch 1, 2 |
Jan 10 | Uninformed search | R&N Ch 3-3.4 |
Jan 12 | Informed search | R&N Ch 3.5-3.7 |
Jan 17 | Search for optimization | R&N Ch 4.1, 4.2 |
Jan 19 | Constraint satisfaction problems | R&N Ch 6 |
Jan 24 | Searching under uncertainty | R&N Ch 4.3, 4.4 |
Jan 26 | Game playing | R&N Ch 5 |
Jan 31 | Monte Carlo tree search | |
Feb 2 | Propositional logic | R&N Ch 7 |
Feb 7 | First-order logic | R&N Ch 8, 9 |
Feb 9 | Classical planning | R&N Ch 10 |
Feb 14 | Planning, uncertainty | R&N Ch 13 |
Feb 16 | Midterm | |
Feb 21 | Bayes Nets | R&N Ch 14 |
Feb 23 | Bayes Nets | |
Feb 28 | Reading week | |
Mar 2 | Reading week | |
Mar 7 | Machine learning basics | R&N Ch 20 - 20.2 |
Mar 9 | Unsupervised learning | R&N Ch 20.3 |
Mar 14 | Supervised learning | R&N Ch 18 |
Mar 16 | Temporal inference | R&N Ch 15 - 15.2 |
Mar 21 | Hidden Markov models | R&N Ch 15.3 |
Mar 23 | Utility and decisions | R&N Ch 16 |
Mar 28 | Markov decision processes | R&N Ch 17 |
Mar 30 | MDPs and POMDPs | R&N Ch 17 |
Apr 4 | Reinforcement learning | R&N Ch 21 |
Apr 6 | Natural language processing | R&N Ch 22 |
Apr 11 | Wrap-up |
Coursework
All relevant handouts and other information will be distributed through myCourses.