&nsbp &nsbp

Artificial Intelligence — CSEN 5320 (Fall 2025)

Department of Electrical Engineering & Computer Science

Section: 001 Credits: 3 Mode: Face to Face Room: ENGC 109 Time: TR 09:30–10:45 AM

Instructor Information

  • Instructor: Shaif Chowdhury, PhD
  • Office Location: EC208
  • Student Support Hours (F2F): TR 12:30 PM – 3:00 PM (ENGC 208)
  • Office Phone: —
  • Email: Shaif.Chowdhury@tamuk.edu

A student may request an appointment well in advance by email if posted hours are unsuitable.

Course Description & Prerequisites

Fundamental concepts of intelligent computer systems, knowledge representation, logical reasoning, search strategies, game theory, Bayesian networks, neural networks, reinforcement learning, natural language processing, and current research in AI area.

Prerequisite: Graduate standing in computer science

Textbooks

Required

Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 3rd Edition. ISBN-13: 978-0134610993, ISBN-10: 0134610997

Recommended

Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016 — deeplearningbook.org

This class will also draw material from lecture slides of Magdon-Ismail, resources and slides available at deeplearningbook.org, the book Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola, as well as resources from Wikimedia.

Course Assignments & Assessment

Quizzes, homework, and a semester project. A midterm exam and a comprehensive final exam are included.

Grading Breakdown

Final Exam: Friday, December 5, 8:00–10:30 AM.

Final Letter Grades
A: 90–100
B: 80–89
C: 70–79
D: 60–69
F: < 60

Course Schedule (by Week)

DISCLAIMER — Some aspects of the schedule may be subject to change and will be announced in class and on Blackboard.

The chapters are based on Artificial Intelligence: A Modern Approach
WeekTopicsChapter
1Introduction1
2Search3
3Logical Agents7
4Bayesian Networks14
5Introduction to Learning18
6Linear Models & Clustering18
7Deep Learning Introduction and Midterm Exam19
8Model Complexity18
9Reinforcement Learning19
10Natural Language Processing21
11Computer Vision22
12Representation Learning24
13AI Ethics26
14Final Project Presentations—
15Review & Final Project Presentations—
16Review & Final Exam—

Course & University Policies

Instructor’s Policies

Attendance

Attend regularly and participate in discussions and exercises. Notify the instructor in advance of planned absences for valid reasons.

Late/Missing Work/Extra Credit

Late assignments accepted up to one week after the posted due date, submitted in the specified manner.

Use of AI in the Classroom

AI tools (e.g., ChatGPT) are not permitted for quizzes or in-class exams. Their use in projects and homework is allowed; students are responsible for verifying AI-assisted work and using it responsibly.


University Policies

University Support Services

Pathways Academic Assistance Center
  • Tutoring & academic support (in-person/online); peer tutors and study skills
  • Location: Jernigan Library 220 • Phone: 361-593-5223
  • Email: paactutoring@tamuk.edu • Website
  • Appointments • IG: tamuksuccess_squad
Writing Center
  • Consultations at any stage of writing
  • Location: Jernigan Library 217 • Phone: 361-593-2744
  • Email: success@tamuk.edu • Website
Success Peer Mentoring
  • Connections to campus/community; time management, motivation, study strategies
  • Location: Jernigan Library 210 & 242 • Phone: 361-593-5800
  • Email: success@tamuk.edu • Website
Jernigan Library
  • Books, laptops, calculators, reserves; computer lab; South Texas Archives
  • Location: 1050 N. University Blvd. • Phone: 361-593-3319
  • Library • LibGuides •