CS 348: Introduction to Artificial Intelligence Fall 2025

Course Description:

This course provides a general introduction to major problems in, approaches to, and applications of Artificial Intelligence  (AI) for CS majors at a moderately advanced undergraduate level and very basic graduate level.

Course Outcomes:

Students should develop an understanding of the key problems addressed by Artificial Intelligence, and of some of the important approaches to, and technologies of, AI, and of how they can be applied to these problems — as well as their current limitations.  Students will gain hands-on experience with these technologies through building and experimenting with small AI systems or system components.

Outline of Topics:

Introduction: The challenge of exponentials; brief history of AI.

Search: Blind search, including breadth-first and depth-first search, and  iterative deepening; heuristic search, including greedy and A* algorithms; local search and constraint propagation.

Adversarial search: Game-playing and mini-max; heuristics in adversarial search; alpha-beta pruning; games with probabilities.

Knowledge representation and logic: Propositional logic; entailment and inference; limitations of propositional logic; first-order predicate calculus; inference methods, forward and backward chaining; knowledge representation and knowledge bases.

Machine learning (part 1): Basic probability; supervised vs. unsupervised learning; simple induction techniques: naïve Bayes, nearest neighbor, decision trees and random forests; clustering algorithms; developing and assessing ML systems.

Machine learning (part 2): Perceptrons; neural networks; introduction to transformers and LLMs; reinforcement learning.

Time permitting, the course will additionally touch on (and may include guest lectures in) aspects of planning; probabilistic reasoning; computer vision; natural language processing; and robotics.

Course Work and Structure:

Course work will consist of 5-6 programming assignments, plus in-class, closed-book, paper-and-pencil mid-term and final examinations.

Grading: Homework projects, 50%; final exam, 30%; mid-term exam, 15%; class participation, 5%.  (Subject to change.)

Optional Text:

Russell  &  Norvig,  Artificial  Intelligence: A  Modern Approach,  3rd   edition,  online  at https://people.engr.tamu.edu/guni/csce642/files/AI_Russell_Norvig.pdf.  (This is an older  edition;  since  it  is  hosted  by  Texas A&M  University,  I  assume  this  is  not  a copywrite violation and the authors are OK with it.)

Approximate Course Schedule:

The calendar below maps class meetings to the general topic areas listed above; as the term progresses it will updated with specific topics as we address them, along with homework project due dates.

Please note days when the class will not meet below.

The mid-term exam is scheduled for Tue Oct 21 during class.

The final exam is scheduled for Thu Dec 11, 12 noon – 2 pm.

Week 1

Tue 9/16 – Introduction: The challenge of exponentials; brief history of AI Thu 9/18 – Search

Week 2

Tue 9/23 – NO CLASS (Rosh Hashanah)

Thu 9/25 – Search

Week 3

Tue 9/30 – Search

Thu 10/2 – NO CLASS (Yom Kippur)

Week 4

Tue 10/7 – Adversarial search

Thu 10/9 – Knowledge representation and logic

Week 5

Tue 10/14 – Knowledge representation and logic

Thu 10/16 – Knowledge representation and logic

Week 6

Tue 10/21 – MIDTERM EXAM

Thu 10/23 – Machine learning, part 1

Week 7

Tue 10/28 – NO CLASS or GUEST LECTURE Thu 10/30 – Machine learning, part 1

Week 8

Tue 11/4 – Machine learning, part 1

Thu 11/6 – Machine learning, part 1

Week 9

Tue 11/11 – Machine learning, part 2

Thu 11/13 – Machine learning, part 2

Week 10

Tue 11/18 – Machine learning, part 2

Thu 11/29 – Machine learning, part 2

Week 11 (NO CLASS - Thanksgiving Break)

Tue 11/25 – NO CLASS (Thanksgiving Break)

Thu 11/27 – NO CLASS (Thanksgiving Break)