CSI 4103 / 5138: Great Algorithms

University of Ottawa, Fall 2026

Recent Announcements

Teaching Staff

name	email	office	office hour
Andrej Bogdanov instructor	abogdano@uottawa.ca	SITE 5068	Mo 3–5
TBA corrector	tba@uottawa.ca

Course Description

Great algorithms are like great works of art. Some are instant hits. Others are challenging at first but grow on you with time. What they have in common is that they change the way we view the world.

Behind every computing advance of the last century stands a great algorithm: error-correction for reliable communication, spectral decomposition and the Fourier transform for data analysis, public-key encryption for e-commerce, gradient descent and backpropagation for machine learning. The aim of the course is to provide a broad perspective on what makes these algorithms tick and the variety of contexts in which they apply.

Schedule

	week	topic
1	Sep 9	Gaussian elimination
2	Sep 16	Entrance Exam Gradient descent
3	Sep 23	Spectral decomposition
4	Sep 30	Fourier transform
5	Oct 7	Backpropagation Review 1
	Oct 14	Midterm Exam 1 Backpropagation (cont'd)
6	Oct 21	Secure multiparty computation
	Oct 28	Reading Week
7	Nov 4	Error correction
8	Nov 11	Markov Chain Monte Carlo
9	Nov 18	Belief propagation Review 2
	Nov 25	Midterm Exam 2 Belief propagation (cont'd)
10	Dec 2	Public-key encryption
	Dec 9	Project presentations

Homeworks ane Exams

Homeworks won't be graded. The problems are provided for practice only. I strongly encourage you to attempt the questions and write solutions. You are encouraged to seek feedback from the corrector. If you choose to please do so within a week of posting.

The midterm exams are in-class on Oct 14 and Nov 25, respectively, from 8.30 to 9.50.

• Homework 1 | out Sep 23 | solutions
• Practice Midterm 1 | out Sep 30 | solutions
• Homework 2 | out Nov 4 | solutions
• Practice Midterm 2 | out Nov 11 | solutions

Course Information

• Lectures Wednesdays 8.30-11.20 in FSS 1007.
• Prerequisites Proficiency in discrete math, basic linear algebra, and probability is assumed. Algorithms at the level of CSI 3105 is helpful but not required. A short entrance exam on Sep 16 will assess your preparedness for the course.
• Grading Your grade will be determined from two midterm exams (30% each), and a final project report and presentation (40%). The entrance exam will count towards a 5% bonus.
• Final project Choose your favorite great algorithm and tell us about it. If you don't have one yet suggestions will be provided around the time of reading week. Projects can be done in groups of two to three. The project grade be be based on your in-class presentation, your project report, and your participation and interest in the other presentations.
• Discussion You can use this piazza forum for homework collaboration and other discussions.

References

Notes will be provided for every lecture. Here are some additional references.

• Sanjoy Dasgupta, Christos Papadimitriou, Umesh V. Vazirani: Algorithms. McGraw-Hill Education, 2006.
• Anupam Gupta: Advanced Algorithms. Lecture notes.