ENCE 452: Artificial Intelligence

Instructor:

Liang Zhang, Assistant Professor

Prerequisites

ECE 220 and STAT 346, or permission of instructor.

Objectives

This course provides basic theory and important applications. Topics include probability concepts and axioms; stationarity and ergodicity; random variables and their functions; vectors; expectation and variance; conditional expectation; moment-generating and characteristic functions; random processes such as white noise and Gaussian; autocorrelation and power spectral density; linear filtering of random processes, and basic ideas of estimation and detection.

Location

EASC 1066

Time

Mon/Wed/Fri 2:00-2:50 pm

ENCE 452 Syllabus

ENCE 452 Lecture Notes

Lectures Download Links
Lecture 0 Lecture 0 (pdf)
Lecture 1 Lecture 1 (pdf)
Lecture 2 Lecture 2 (pdf)
Lecture 3 Lecture 3 (pdf)
Lecture 4 Lecture 4 (pdf)
Lecture 5 Lecture 5 (pdf)
Lecture 6 Lecture 6 (pdf)
Lecture 7 Lecture 7 (pdf)
Lecture 8 Lecture 8 (pdf) Video part 1 (mp4) Video part 2 (mp4)
Lecture 9 Lecture 9 (pdf)
Annex (Generating RVs) Annex (pdf)
Lecture 10 Lecture 10 (pdf)
Lecture 11 Lecture 11 (pdf)
Lecture 12 Lecture 12 (pdf)
Lecture 13 Lecture 13 (pdf)
Lecture 14 Lecture 14 (pdf)
Lecture 15 Lecture 15 (pdf)
Lecture 16 Lecture 16 (pdf)
Formula Sheetpmf and pdf for exam

ENCE 452 Projects

  1. Project 1

    Project     Solutions

  2. Project 2

    Project     Solutions

  3. Project 3

    Project     Solutions

ENCE 452 Homework

Course Schedule

Week Lecture Topic Chapter
1 — 08/25 Lecture 0, Lecture 1 Introduction to AI: Past, Present, Future Agents and environments 1, 2
2 — 09/01 Lecture 2 Uninformed Search 3.1-3.3
3 — 09/08 Lecture 2A Uninformed Search Strategies 3.4
4 — 09/15 Lecture 2B A* Search and Heuristics and Heuristic Functions; Local search; Search-based agents 3.5-3.6; 4.1
5 — 09/22 Lecture 3 Adversarial Search 5
6 — 09/29 Lecture 4 Knowledge Based Agents 7.1-7.3
7 — 10/06 Lecture 4A Propositional Logic Inference 7.4-7.5
8 — 10/13 Midterm
9 — 10/20 Lecture 5 Quantifying Uncertainty 12.1-12.5
10 — 10/27 Lecture 6 Bayes Nets: Syntax and Semantics 13.1-13.3
11 — 11/03 Lecture 6A Bayes nets: Exact Inference and Approximate Inference 13.3-13.4
12 — 11/10 Lecture 6B More on Bayes nets 13.5-13.6
13 — 11/17 Lecture 7 Reinforcement Learning I 22.1-22.4
14 — 11/24 Lecture 7A Reinforcement Learning II 22.4-22.5
15 — 12/01 Lecture 8 Decision Tree Learning, Neural Network Learning 19.1-19.3; 19.7
16 — 12/08 Final Exam

Last day of class is December 12, 2025.