CS 4300-001 Spring 2019 Artificial Intelligence

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This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm, with applications ranging from diagnosis to game-playing to robotics. This course is built around several multi-part programming projects, based on the game of Pacman.

Coursework will consist of three kinds of assignments. Programming projects will be in Python. Programming projects may be done in teams of two, or done solo. Written homeworks will be given most weeks. We will additionally have in-class quizzes most weeks.

Prerequisites: This course has substantial elements of both programming and mathematics, because these elements are central to modern AI.

  • CS 3505 (Prior programming experience is expected; although we don't expect that you know Python, we do expect you to be able to pick it up rapidly.)
  • CS 3130 Engineering Probability and Statistics
  • CS 4150 Algorithms

Expectations: You are expected to come to class prepared by reading the assigned sections of the book ahead of time.

Class Location: WEB L103

Instructor: Tucker Hermans

Office Hours: Thursdays 15:30 - 16:30 or by appointment

Location: MEB 2164 (Second Floor, Far West Hallway)

It is best to contact me through canvas, as this will correctly classify your email so that I'm less likely to miss it.

TAs: 

Rushit Sanghrajka

Office Hours: Tuesdays 15:00 - 17:00 & Thursdays 9:30 - 10:30

Location: 3161 MEB

Please feel free to contact me by Canvas mail.

Mohanraj Devendran Shanthi 

Office Hours: Mondays 12:00 - 14:00 & Wednesdays 12:30 - 13:30

Location: MEB 2176

Please contact me via canvas mail, so that I won't miss your message.

Textbook

The official textbook for this course is:

Artificial Intelligence: A Modern Approach (Third Edition)
by Stuart Russell and Peter Norvig. Prentice Hall, 2009.

Be sure you have the Third Edition. It is BLUE, not GREEN or BURGUNDY: the other editions are not sufficient.

We will also occasionally have readings from two books available online:

Reinforcement Learning: An Introduction
by Richard S. Sutton and Andrew G. Barto. MIT Press, 1998.

and

Deep Learning

by Ian Goodfellow, Yoshua Bengio, and Aaroun Courville. MIT Press, 2016.

Class Schedule

See a detailed, but open to change, class schedule here: Course Schedule

Grading

Overall grades will be determined from:

  • Homework assignments (30%)
  • Programming projects (35%)
  • In-class quizzes (35%)

Assignments (homework and projects) must be turned in electronically by midnight on the listed due date. Assignments may be turned in up to two days late. A penalty of 10% per day will be assessed. The weekend counts as one day.

There is no promise quizzes will be announced prior to class. Quizzes cannot be made up if class was missed without a documented, exceptable excuse. If you know you need to miss class, let the teaching staff know before you miss class and we can arrange a make up.

There is no final exam.

Course Policies

Cheating: Any assignment or exam that is handed in must be your own work. However, talking with one another to understand the material better is encouraged. Recognizing the distinction between cheating and cooperation is very important. If you copy someone else's solution, you are cheating. If you let someone else copy your solution, you are cheating. If someone dictates a solution to you, you are cheating. Everything you hand in must be in your own words, and based on your own understanding of the solution. If someone helps you understand the problem during a high-level discussion, you are not cheating. Any student who is caught cheating will be given an E in the course and referred to the University Student Behavior Committee. Please don't take that chance - if you're having trouble understanding the material, please let us know and we will be more than happy to help.

Academic Misconduct

The SoC policy states: ”As defined in the University Code of Student Rights and Responsibilities, academic misconduct includes, but is not limited to, cheating, misrepresenting one’s work, inappropriately collaborating, plagiarism, and fabrication or falsification of information. It also includes facilitating academic misconduct by intentionally helping or attempting to help another student to commit an act of academic misconduct. A primary example of academic misconduct would be submitting as one’s own, work that is copied from an outside source.”

School of Computing Policies and Guidelines: https://www.cs.utah.edu/~germain/SoC_Guidelines

College of Engineering Guidelines: https://www.coe.utah.edu/students/current/semester-guidelines/

 

University’s Accommodation Policies

The University of Utah seeks to provide equal access to its programs, services and activities for people with disabilities. If you will need accommodations in the class, reasonable prior notice needs to be given to the Center for Disability Services, 162 Olpin Union Building, 801-581-5020. CDS will work with you and the instructor to make arrangements for accommodations. All written information in this course can be made available in alternative format with prior notification to the Center for Disability Services.

Course Summary:

Date Details Due
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