University of Utah

School of Computing

Artificial Intelligence

CS 5300

Fall Semester 2014

WEB 2250   TH 10:45-12:05

Instructor: Thomas C. Henderson

Overview of Course

Course Objectives

Survey Artificial Intelligence, Machine Learning and Natural Language Processing :


Prerequisites: (No graduate students from any department); C- or better in (CS3505 and (CS3130 or ECE 3530) and CS4150) and (undergraduate full major status in Computer Science or Computer Engineering).

Course Description

We will work on the problems and solutions of modern artificial intelligence, machine learning and natural language processing. 

Software Used to Support Class

Students will develop codes in Matlab. 

Required Materials

We will use:

Artificial Intelligence, Russell and Norvig, (required)


There are 3 major types of assignments (use the Lab Report Format for Problem and Extra Credit reports):

Class Syllabus

The lectures will cover the text on the following schedule (may vary some during semester to accommodate progress):



  Material (G. Weiss )

     Problem Assignments




August 26 - 28

Intelligent Agents

Chapters 1,2

Assigned: A1, EC1

September 2 - 4, 9 - 11

Problem Solving

Chapters 3,5

Assigned: A2, EC2

September 16 - 18, 23 - 25

Logical Agents

Chapters 7,8,9

Assigned: A3, EC3

September 30 - Oct 2, Oct 7 - 9

Probability and Bayesian Networks

Chapter 13,14

Assigned: A4, EC4

October 14 - 18

Fall Break

Oct 21 - 23, Oct 28 - 30

Bayes Nets, Markov Decision Processes

Chapters 14,16,17

Assigned: A5

Nov 4 -6, 11 - 13, 18 -20


Chapters 18,21

Assigned: A6, A7, EC5

Nov 25, Dec 2 - 4, 9 - 11

Natural Language Processing

Chapter 22

Assigned: A8, EC6


Class Schedule and Assignments

The lectures and assignments will cover the text as we progress through the semester.  Assignments will usually be handed out on Tuesday and due on a Thursday after the material is covered.



Thomas C. Henderson, Professor






Office Hours (2871 WEB): By appointment.







Office Hours :


Grading Information

The grading distribution will be as follows:

You are expected to make a good effort on all assignments and in-class discussion based on a careful reading of the assigned material.  I will assign a grade based on how reasonable your solution is given the difficulty of the assignment, the time required, and the style and content of the solution.  My goal is to look at all your work, and to assign a grade based on your participation, effort and results.  It's better to ask questions before and during an assignment, than to try and understand what went wrong after it's due.  The proportions given above delineate how I intend to apportion the weight of the various work in the course.

Assignment Due Time

Unless otherwise stated in an assignment, all assignments will be due by classtime on the assignment due date.   You should handin PDF's and code.  The time that we use for an assignment is the submit time.  I may ask for supporting material as well (Matlab codes, images, math analysis, etc.).

Appeals Procedure

See the Code of Student Rights and Responsibilities, or the Class Schedule for more details.

Appeals of Grades and other Academic Actions

If a student believes that an academic action is arbitrary or capricious he/she should discuss the action with the involved faculty member and attempt to resolve.  If unable to resolve, the student may appeal the action in accordance with the following procedure:

  1. Appeal to Department Chair who should be notified in writing within 40 working days; chair must notify student of a decision with 15 days.  If faculty member or student disagrees with decision, then,
  2. Appeal to Academic Appeals Committee (see flyers posted in MEB and EMCB for members of committee).  See II Section D, Code of Student Rights and Responsibilities for details on Academic Appeals Committee hearings.

Assignment Late Policy

No late work is accepted. 

Individual Work

The purpose of the assignments is to improve your skills at solving problems and demonstrating that you understand the class material. Collaboration with other class members is acceptable in understanding problems or software tools. For any individual assignments or work turned in, you must do your own work. Using someone else's work or giving someone else your work is considered plagiarism and will be dealt with using standard College and University procedures (i.e., failure of assignment and class). 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." (See cheating_policy.pdf and SoC_ack_form.pdf in Link to Class Info and Docs.)


See university web page for the full academic calendar (Calendar web page).  See the university web page for a copy of the withdraw guidelines as well, or see the Student Code.

See the college web page for more Guidelines.

American with Disabilities Act (ADA)

The University conforms to all standards of the ADA. If you wish to qualify for exemptions under this act, notify the Center for Disabled Students Services, 160 Union
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, 581-5020 (V/TDD).  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.