| name | office | office hour | |
|---|---|---|---|
| Andrej Bogdanov instructor |
abogdano@uottawa.ca | SITE 5068 | Fr 3–5 |
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.
| week | topic | ||
|---|---|---|---|
| 1 | Sep 4 | Gaussian elimination | |
| 2 | Sep 11 | Gradient descent | |
| Sep 18 | No class | ||
| 3 | Sep 25 | Spectral decomposition | |
| 4 | Oct 2 | Fourier transform | |
| 5 | Oct 9 | Backpropagation | |
| Oct 16 | Reading Week | ||
| 6 | Oct 23 | Public-key encryption | |
| 7 | Oct 30 | Secure multiparty computation | |
| 8 | Nov 6 | Error correction | |
| 9 | Nov 13 | Randomized response | |
| 10 | Nov 20 | Markov Chain Monte Carlo | |
| 11 | Nov 27 | Belief propagation | |
| TBA | Project presentations |
You are encouraged to collaborate on the homeworks as long as you write your own solutions and acknowledge your collaborators. I discourage you from looking up solutions to homework problems online, unless you have exhausted all other resources. If you do so, please provide proper credit and make sure you understand the solution before you write it.
Notes will be provided for every lecture. Here are some additional references. These will be updated later.