CSE 511A: Introduction to Artificial Intelligence – Fall 2017

Instructor: Professor Roman Garnett
TAs: Zimu Wang (zimu.wang), Matthew Ranftle (matthew.ranftle), Jack Robards (jrobards)
Time/Location: Monday/Wednesday 4–5:30pm, Hillman Hall 70
Office hours (Garnett): Wednesdays 5:30–6:30pm, Hillman Hall 70 then Jolley 504
Office hours (TAs): Wednesdays 2:30–4pm, outside Jolley 515
Office hours (TAs): Thursdays 1–4pm, outside Jolley 515
Office hours (TAs): Fridays 2–4pm, Jolley 431
syllabus
Piazza message board Please ask all questions on Piazza!


Description

The discipline of artificial intelligence (AI) is concerned with building systems that think and act like humans or rationally on some absolute scale. This course is an introduction to the field, with special emphasis on sound modern methods. The topics include knowledge representation, problem solving via search, game playing, logical and probabilistic reasoning, planning, machine learning (decision trees, neural nets, reinforcement learning, and genetic algorithms) and machine vision. Programming exercises will concretize the key methods. The course targets graduate students and advanced undergraduates. Evaluation is based on programming assignments, a midterm exam, and a final exam.

Prerequisites

If you are unsure about any of these, please speak with the instructor.

Assignments

Please post all questions to Piazza!

You can find autograder information here.

Project 0 is due Monday, 4 September, 2017. Project 0 leaderboard
Project 1 is due Monday, 18 September, 2017. Project 1 leaderboard
Project 2 is due Monday, 9 October, 2017. Project 2 leaderboard
Project 3 is due Monday, 30 October, 2017. Project 3 leaderboard

Lectures

Lecture 1: Introduction

Monday, 28 August 2017
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Lecture 2: Uninformed Search

Wednesday, 30 August 2017
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Lectures 3–4: Informed Search

Wednesday, 6 September 2017
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Lecture 5: Constraint Satisfaction Problems

Wednesday, 13 September 2017
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Lecture 6: Constraint Satisfaction Problems II

Monday, 18 September 2017
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Lecture 7: Adversarial Search

Wednesday, 20 September 2017
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Lecture 8: Expectimax

Monday, 25 September 2017
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Lecture 9: Markov Decision Processes I

Wednesday, 27 September 2017
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Lecture 10: Markov Decision Processes II

Monday, 2 October 2017
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Lecture 11: Reinforcement Learning I

Wednesday, 4 October 2017
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Lecture 12: Reinforcement Learning II

Wednesday, 18 October 2017
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Lecture 13: Probability

Monday, 22 October 2017
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Resources

This course is based on the CS 188 course at UC Berkeley. You may find lectures, slides, and more there.

Books

The required book for this course is Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig. Either the second or third edition is fine. This is a classic textbook and highly recommended!

Another good reference for reinforcement learning is Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. The book is available online here.

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