ENEM 645: Principals of Communications Networks

Instructor:

Liang Zhang, Assistant Professor

Prerequisites

ENGE 330 (all with grade of C or better), or permission of instructor.

Objectives

This course covers advanced subjects in computer networks.

Topics will include Internet architecture and core protocols for congestion control, forwarding, naming, and routing; approaches to achieve reliability, scalability, and security; and design of hyperscale cloud networks, data centers, wireless networks, content delivery, enterprise networks, quality of service, and network security. Material will range from the classics to the latest results, and from analytical foundations to systems design and real-world deployment.

Location

EASC 1082

Time

Mon/Wed/Fri 2:00 pm — 2:50 pm

ENEM 645 Syllabus

ENEM 645 Lecture Notes

Lectures Download Links
Lecture 0 Lecture 0 (pdf)
Lecture 1 Lecture 1 (pdf)
Lecture 1B Programming in Python (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

Projects

  1. Project 1

    Project     Solutions

  2. Project 2

    Project     Solutions

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.