Category Archives: course archive

The course is available on Moodle.
The course outline can be accessed here.

Lectures: Tue/Thr 1:30 pm – 2:45 pm, in A580

Topics: Fourier transforms, the sampling theorem, Shannon formula for the capacity of a communication channel.

References:

• For the Fourier transform, Section 2.6 in Algorithms, by Dasgupta, Papadimitriou, and Vazirani. See an excerpt from from the text here, provided to you under fair dealing.
• For the capacity of a channel, see Information and Measurement, Chapter 8, by Lesurf.

Homework:

• Questions
• Data for the WiFi measurements
• Description of the data, from CRAWDAD.
• Paper about WiFi

Lectures for the week: Sep 13-15.
Topic: an introduction to classes.
Text: Chapter 7 in Skansholm.
Notes available here (updated Sept 18).
Source code including homework available at cloud9 or as a zip file (updated Sept 18).

Course outline.

Fall 2025: the course materials are available on Moodle to registered students.
Course outline
Textbooks:

• Main text: Computer Networking, Principles, Protocols and Practice 3rd Ed 2019, by Bonaventure, available online here
• Supplementary texts:
  • An Introduction to Computer Networks, by Dordal, available at intronetworks.cs.luc.edu
  • Computer Networks: A Systems Approach}, by Peterson and Davie, 2019 update, available here
  • Computer Networks 5th Ed, by Tanenbaum and Wetherall
  • Computer Networking: A Top-Down Approach (7th Edition) by Kurose and Ross.

Lectures: Mon/Wed 1:30 pm – 2:45 pm, in B660

The course materials are available on moodle to registered students.
Textbooks:
Discrete Mathematics and Its Applications – 8th Ed, by Rosen (older
editions OK).
Book of Proof – 2nd Ed, by Hammack, available at http://www.
people.vcu.edu/~rhammack/BookOfProof/ (CC Licence).
Course outline.

An older offering of the course is accessible
here.

All course resources are available on moodle to registered students.
Texts:
Open Data Structures by Pat Morin.
C++ Primer, 5th Ed, Lippman, Lajoie, Moo.
Mastering C++
Course outline

Lecture notes (jupyter)

This course is about designing approximation algorithms for difficult
optimization problems for which no optimal algorithms with running
time polynomial in the problem size are known. Approximation
algorithms find feasible solutions that may not be optimal but are not
too far from the optimal one.

We overview various interesting
algorithm design techniques that may prove extremely useful to
graduate students tackling research questions in various fields and to
undergraduates who may encounter interesting problems in their future
projects in the industry.

[course wbpage]

Course resources are on Moodle.
The course outline: [PDF]

Course resources are on Moodle.
Course outline: [PDF]