Syllabus

Class Times:

Mondays, Wednesdays, and Fridays 11:00–11:50am

First class:

January 6, 2020

Location:

BS M 149

Instructor:

James Wright (james.wright@ualberta.ca)

Office:

ATH 3-57

Office hours:

Available after class on Mondays and Fridays, and by appointment.

Lab:

Thursdays 5:00pm to 8:00pm (BS M 149)

eClass

https://eclass.srv.ualberta.ca/course/view.php?id=57991

Description

Introduction to modern artificial intelligence techniques, with a focus on probabilistic reasoning. Specific topics include uninformed and heuristic search, probabilistic modeling and reasoning, causal inference, deep learning, Bayesian learning, reinforcement learning, and multiagent systems. The course will emphasize the importance of appropriate choices of formal model.

Topics

• Uninformed and Heuristic Search
• Probabilistic Modeling and Reasoning
• Bayesian Learning
• Causal Inference
• Deep Learning
• Reinforcement Learning
• Multiagent Systems

Objectives

After taking this survey course, you will understand the foundations of modern probabilistic artificial intelligence and how they relate to each other, in preparation for taking more advanced courses. You will understand the strengths and weaknesses of the broad families of representations in each area. You will be able to choose appropriate models in application domains, and be able to encode specific problems in those models effectively.

Grading

Grade breakdown

• Assignments: 30%
• Midterm exam: 30%
• Final exam: 40%

Late assignments

Assignments are to be handed in electronically via eClass by the start of lecture on the due date. Late assignments will have 20% deducted for each day that the assignment is late, up to a maximum of three days late.

Academic conduct

Submitting the work of another person as your own constitutes plagiarism. The department is very strict about plagiarism and other academic misconduct: ALL forms of cheating are referred to the Dean’s office.

The rules for this course allow consultation collaboration. The specific rules are:

• You may discuss assignments and solutions with your classmates.
• Limit discussion to an informal verbal level. DO NOT exchange written text or source code: you can discuss assignments, but don’t look at each other’s answers or give step-by-step instructions.
• You must list all of the other students that you discussed an assignment with.
• The written part of the assignments (including source code) must be completed individually.

Texts

Students are responsible only for material that is presented in class. Slides will be made available on eClass on the day of the corresponding lecture.

Optional readings will be provided from the following texts, all of which are available online:

• David Poole and Alan Mackworth, Artificial Intelligence: Foundations of Computational Agents, 2nd edition.
• David Barber, Bayesian Reasoning and Machine Learning.
• Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning.
• Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd edition.
• Yoav Shoham and Kevin Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations.