CSCI 4450/8456-850 Artificial Intelligence (Spring 2026)

Undergraduate/Graduate course, UNO, 2026

Artificial intelligence (AI) is rapidly advancing due to breakthroughs in data accessibility, computing power, and algorithmic sophistication. This has contributed to a surge in AI applications across various domains, including search, machine learning, natural language processing, robotics, and computer vision. This course provides a foundational understanding of AI, exploring core concepts in problem-solving, heuristic search, knowledge representation, deduction, planning, and learning. Through hands-on programming assignments, students will develop autonomous agents capable of making informed decisions in complex environments. Upon course completion, students will be able to develop intelligent systems capable of autonomous decision-making and learning in fully informed, partially observable and adversarial settings. They will also master constraint programming techniques to address complex optimization challenges. This coursework provides a strong foundation for pursuing advanced AI research and practical applications. The main learning objectives of the course are to identify problems suitable for artificial intelligence techniques and apply basic AI techniques and evaluate the suitability of more advanced methods and contribute to the design of systems that exhibit intelligent behavior and learn from experience.

Administrative Information

  • Instructor: Pei-Chi Huang
  • Email: phuang at unomaha dot edu
  • Office Hour: Wednesday 12 - 2 PM via Zoom or by appointment
  • Class Info: Students will learn from the recorded videos. (Location: Totally Online)
  • Course Schedule

Academic Integrity

You may discuss the homeworks and assignments with anyone and use any reference materials but provided you do not copy any other person’s work. We will follow the University Policy on Academic Integrity regarding any cheating and plagiarism. Take the time to familiarize yourself with the contents of this page, as you are responsible for its contents.

Accommodations

Reasonable accommodation is provided for students who are registered with Accessibility Services Center (ASC) and make their requests sufficiently in advance. For more information, contact ASC (Location: 104 H&K, Phone: 402.554.2872, Email: unoaccessibility@unomaha.edu)


Schedule

This schedule, and the links contained in it, are subject to change during the semester. Exam dates, however, are final. Readings from additional sources are linked to the schedule. All reading assignments are required and are expected to be completed before class on the scheduled day.

Week TopicAssignment
(to be completed before class)
1Jan. 12 - Jan. 18[Introduction to AI]Reading: Canvas - “Start here”
ch.1
2Jan. 19 - Jan. 25Martin Luther King Day
[Uninformed Search]
Reading: ch.2 & ch.3.1-3.4
Homework 1 Available
Project 1 Available
3Jan. 26 - Feb. 1[Uninformed Search]
[Informed Search]
Reading:ch.3.1-3.4 & ch.3.5-3.6
4Feb. 2 - Feb. 8[Informed Search]
[Constraint Satisfaction Problems]
Reading: ch.6
Homework 1 Due (Gradescope)
Homework 2 Available
5Feb. 9 - Feb. 15[Constraint Satisfaction Problems] Homework 2 Due (Gradescope)
Project 1 Due, 11:59pm
6Feb. 16 - Feb. 22 Midterm I
[Adversarial Search]
Reading: ch.5.1-5.2
Homework 3 Available
Project 2 Available
7Feb. 23 - Mar. 1[Adversarial Search]
[Uncertainty / ExpectiMax]
Reading: ch.5.3
8Mar. 2 - Mar. 8[Uncertainty / ExpectiMax]
[Markov Decision Processes]
Reading: ch.17.1-17.2
Homework 3 Due (Gradescope)
Homework 4 Available
9Mar. 9 - Mar. 15[Markov Decision Processes]Reading: ch.17.1-17.2
Project 2 Due, 11:59pm
10Mar. 16 – Mar. 22Spring Vacation (Student Holiday) Homework 4 Due (Gradescope)
11Mar. 23 - Mar. 29[Reinforcement Learning]
Midterm II
Reading: ch.22.1-22.2
Homework 5 Available
Project 3 Available
12Mar. 30 - Apr. 5[Reinforcement Learning]Reading: ch.22.1-22.2
13Apr. 6 - Apr. 12[Probabilities and Bayes Nets]Reading: ch.12.2-12.6; ch.13.1-13.3
Homework 5 Due (Gradescope)
Homework 6 Available
14Apr. 13 - Apr. 19[Probabilities and Bayes Nets]Reading: ch.12.2-12.6; 13.1-13.3
15Apr. 20 - Apr. 26[Probabilities and Bayes Nets]Reading: ch.21.1-21.6
Homework 6 Due (Gradescope)
Project 3 Due, 11:59pm
16May. 1 Midterm III Reading: textbook, supplemental materials, and all slides