CS 6300-001 Spring 2018 Artificial Intelligence

CS 6300-001 Spring 2018 Artificial Intelligence

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 two 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.

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 2230

Instructor: Tucker Hermans

Office Hours: Thursdays 15:00 - 16:00 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.


Amanda Lee

Office Hours: Monday 2~3pm, Thursday 10~11am

Location: MEB 3115
Contact me by Canvas mail.

Tejas Hirekatur Sreedhar

Office Hours: Tuesday 11AM -12 PM, Wednesday 2-3PM

Location: MEB 3115


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:

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

This book is available online. 

Class Schedule

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

For final exam time please follow the registrar's calendar.


Overall grades will be determined from:

  • Homework assignments (30%)
  • Programming projects (30%)
  • Exams 1-2 (20%)
  • Final exam (20%)

Assignments (homework and projects) must be turned in electronically by midnight on the listed due date. With the indicated exceptions for homeworks, 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 a moratorium on complaints about grading, etc., of one week.

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_Spring_2017

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
CC Attribution This course content is offered under a CC Attribution license. Content in this course can be considered under this license unless otherwise noted.