CS 4222/5222 - Artificial Intelligence (Fall 2023)

Course Description

Principles and programming methods of artificial intelligence. Knowledge representation methods, state space search strategies, and use of logic for problem-solving. Applications are chosen from among expert systems, heuristic search, planning, natural language understanding, uncertainty reasoning, machine learning, and robotics. Lectures and labs will utilize suitable high-level languages (e.g., Python or LISP)


Dr. Andrew M. Sutton
Email: amsutton@d.umn.edu
Tel: 218.726.7978
Office: 311 Heller Hall
Office Hours: MWF 10:00-11:00

Teaching Assistant

Lovis Armah
Email: armah007@d.umn.edu
Office Hours: TBA

Meeting Times and Locations

  Day Time Location
Lecture M W F 9:00-9:50 Heller Hall 306
Lab Th 18:00-18:50 M W Alworth Hall 175


  • CS 2511 - Software Analysis and Design
  • CS 2531 - Discrete Structures or Math 3355 - Discrete Math

A grade of C- or better is required in all prerequisite courses.

Important note: The computer science bachelor's degree program at UMD is accredited by CAC (the Computing Accreditation Commission). One of the CAC requirements is that all students must satisfy the prerequisites in order to be admitted to a course, so if you have not passed the prerequisite courses, you must drop this course (if you have any questions about this, please see the instructor after the lecture or during office hours).


Russell, S. and Norvig, P. Artificial Intelligence: A Modern Approach, Fourth Edition, 2020, Prentice Hall, NJ, ISBN: 978-0134610993.

Web site: http://aima.cs.berkeley.edu

Course Content and Objectives


Artificial Intelligence is the study of intelligent behavior of machines. Broadly speaking, this is understanding how to make machines and software that can reason, learn, and evolve like animals or humans.

AI has a long and interesting history that arose out of philosophical questions from antiquity and blossomed in the 1950s with the advent of electronic computers and computational research. In this course, we will study the basic ideas behind automated reasoning, heuristic search, representing knowledge effectively, and learning.

The course is structured into three units, with 4-6 topics in each unit. We will discuss each topic in lecture, there will be associated required reading, and possible a related lab or written assignment. Here is a tentative schedule for the semester:

UNIT I, Weeks 1-7: Search

Week Topic
1 Introduction
2 Uninformed+Informed Search
3 Informed Search
4 Iterative Improvement Search
5 Evolutionary Search
6 Adversarial Search

UNIT II, Weeks 8-12: Reasoning

Week Topic
8 Constraint Satisfaction
9 Propositional Logic
10 Propositional Satisfiability
11 Reasoning under Uncertainty
12 Reasoning under Uncertainty

UNIT III, Weeks 13-16: Learning

Week Topic
14 Inductive Learning and Trees
15 Linear Models, kNN, Deep Learning

Objectives and Student Learning Outcomes

This course addresses UMD campus student learning outcomes (SLOs), as well as CS education outcomes specified by the UMD Department of Computer Science and aligned with the standards put forth by the ABET accrediting board.

  • SLO 1: Demonstrate competence in a major field
    • Students will gain knowledge of the core methods of artificial intelligence. They will be able to demonstrate knowledge of important search methods such as BFS, DFS, UCS, and A* search to solve a variety of problems.
  • SLO 3: Think critically and creatively in seeking solutions to practical and theoretical problems.
    • Students will be able to formulate and solve constraint satisfaction problems with computer methods.
    • Students will be able to analyze, design and employ inference on knowledge representations using logical constructs on knowledge representation problems.
    • Students will be able to recognize different problems from the domain of AI, and be able to identify the most appropriate method for solving it.
    • Students will be able to discuss the use of probabilistic methods on problems with significant uncertainty.

Assignments and Grading

Projects and Homework

There will be approximately five lab projects and five written homework assignments worth 20 points each. The labs are programming exercises where we will build concrete instances of algorithms discussed in class. We will use python for every programming exercise. No prior experience with python is necessary.

All assignments must be submitted by their stated due date.

Students enrolled in 5222

If you are a graduate student enrolled in 5222, there will be an extra research project. You will need to complete the following steps.

  • Select a research paper on a particular AI topic. The paper must be approved by the instructor by the first midterm exam.
  • Submit a short report (5-8) pages outlining the paper by the second midterm exam.
  • Give a short (10-15 minute) oral presentation about the paper at the end of the semester


This is an upper division course, and therefore I will not make attendance mandatory. However, classroom participation will be graded and included as part of the assignment component for the computation of the final grades. To allow a fair chance for everyone to participate, I will sometimes draw names at random to ask questions about the material. At the end of the semester, your participation grade will take into account these opportunities, as well as participatory evidence such as attending labs and/or office hours, and engaging in class discussions.


There will be two midterm exams, worth 100 points each and a final exam worth 200 points.

Both midterm exams are closed book, and the final exam will be comprehensive. Exams will not be given early, and makeups must be justified by dire circumstances described to the instructor before the time of the exam. It is Department of Computer Science policy not to return final exams, however they are kept and you can look at your exam in the instructor's office. The UMD Final Examination Policy web page explains the UMD policy about having more than two final exams on a single day, among other things.

Exam Schedule

Exam Points Date
Midterm Exam 1 100 Oct 16
Midterm Exam 2 100 Nov 15
Final Exam 200 Dec 15 (8:00am)

Grading Procedures

Final grades are based on total points distributed approximately as follows:

  1. Assignments and participation (approx. 250 points)
  2. Midterm Exams (200 points)
  3. Final Exam (200 points)
  4. Graduate research project (5222 students only) (150 points)

Grades are assigned based on a percentage of the total points.

  • The A- cutoff is 90%
  • The B- cutoff is 80%
  • The C- cutoff is 70%
  • The D cutoff is 60%
  • Below 60% is an F

University Policies

Student Conduct Code

Appropriate classroom conduct promotes an environment of academic achievement and integrity. Disruptive classroom behavior that substantially or repeatedly interrupts either the instructor's ability to teach, or student learning, is prohibited. Students are expected to adhere to the Board of Regents Policy.

Student Academic Integrity

Academic dishonesty tarnishes UMD’s reputation and discredits the accomplishments of students. Academic dishonesty is regarded as a serious offense by all members of the academic community.

Appropriate Use of Class Notes & Course Materials

Taking notes is a means of recording information but more importantly of personally absorbing and integrating the educational experience. However, broadly disseminating class notes beyond the classroom community or accepting compensation for taking and distributing classroom notes undermines instructor interests in their intellectual work product while not substantially furthering instructor and student interests in effective learning.

Excused Absences

Students are expected to attend all scheduled class meetings. It is the responsibility of students to plan their schedules to avoid excessive conflict with course requirements. However, there are legitimate circumstances that lead to excused student absence from the classroom. These are subpoenas, jury duty, military duty, religious observances, illness, bereavement, and NCAA varsity intercollegiate athletics.

Final Examinations

All 1xxx-5xxx courses offered for undergraduate credit should include a final graded component or end of term evaluation that assesses the level of student achievement of one or more course objectives. All final graded components are to be administered or due at the time and place according to the final exam schedule and not during the last week of class.

Teaching & Learning: Instructor & Student Responsibilities

UMD is committed to providing a positive, safe, and inclusive place for all who study and work here. Instructors and students have mutual responsibility to ensure that the environment in all of these settings supports teaching and learning, is respectful of the rights and freedoms of all members, and promotes a civil and open exchange of ideas.

Sexual Harassment, Sexual Assault, Stalking and Relationship Violence

Sexual harassment means unwelcome sexual advances, requests for sexual favors, and/or other verbal or physical conduct of a sexual nature. Such conduct has the purpose or effect of unreasonably interfering with an individual's work or academic performance or creating an intimidating, hostile, or offensive working or academic environment in any University activity or program. Such behavior is not acceptable in the University setting. See also: Board of Regents Policy.

Equity, Diversity, Equal Employment Opportunity, and Affirmative Action

The University provides equal access to and opportunity in its programs and facilities, without regard to race, color, creed, religion, national origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. Equal Opportunity and Affirmative Action and the Office of Student Conduct & Conflict Resolution are both available to all UMD employees, students, and participants in University-related activities to discuss issues or concerns regarding University policies or practices involving potential bias, discrimination, harassment or retaliation that an individual may have experienced or observed.

Academic Freedom and Responsibility

Thoughtful dialog is a cornerstone of higher education. This expectation is upheld in the University of Minnesota's Board of Regents Policy: Academic Freedom and Responsibility, which says in part:

SECTION II. ACADEMIC FREEDOM. Academic freedom is the freedom, without institutional discipline or restraint, to discuss all relevant matters in the classroom, to explore all avenues of scholarship, research, and creative expression, and to speak or write on matters of public concern as well as on matters related to professional duties and the functioning of the University.

Resources for Students with Disabilities

It is the policy and practice of the University of Minnesota Duluth to create inclusive learning environments for all students, including students with disabilities. If there are aspects of this course that result in barriers to your inclusion or your ability to meet course requirements such as time limited exams, inaccessible web content, or the use of non-captioned videos, please notify the instructor as soon as possible. You are also encouraged to contact the Office of Disability Resources to discuss and arrange reasonable accommodations. Call 218-726-6130 or visit the Disability Resources website for more information.

The instructor reserves the right to make changes to this syllabus or the course calendar at any time, and without prior notice.

Created: 2023-11-27 Mon 10:55