Lectures
| Video Link (Click Lect. #) | Date | Topics |
|---|---|---|
| 0 | 21 January 2021 | Introduction (PDF) |
| 1 | 26 January 2021 | Overview of Machine Learning and Imaging (PDF) |
| 2 | 26 January 2021 | Continuous Mathematics Review (PDF) |
| 3 | 28 January 2021 | From Continuous to Discrete Mathematics (PDF) |
| 4 | 2 Feb 2021 | Discrete Functions (PDF) |
| 5 | 4 Feb 2021 | Introduction to Optimization (PDF) |
| 6 | 9 Feb 2021 | Ingredients for Machine Learning (PDF) |
| 7 | 11 Feb 2021 | Ingredients for Machine Learning, Part II (PDF) |
| 8 | 16 Feb 2021 | Linear and Logistic Classification (PDF) |
| 9 | 18 Feb 2021 | “Deep” Networks: theoretical motivation (PDF) |
| 10 | 23 Feb 2021 | Convolutional Neural Networks (PDF) |
| 11 | 25 Feb 2021 | Convolutional Neural Networks (PDF) |
| 12 | 2 March 2021 | Tools for your Deep Learning Toolbox (PDF) |
| 13 | 4 March 2021 | Automatic Differentiation and Backpropagation (PDF) |
| 14 | 11 March 2021 | CNN implementation details (PDF) |
Lecture Resources
Here are a few links to useful additional material for reading and viewing:
- J. Goodman’s Introduction to Fourier Optics Link
- The Matrix Cookbook Link
- An introduction to conjugate gradient descent without all the pain Link