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:

  1. J. Goodman’s Introduction to Fourier Optics Link
  2. The Matrix Cookbook Link
  3. An introduction to conjugate gradient descent without all the pain Link

Jupyter Notebook Examples

  1. Jupyter Notebook: Tensorflow basic optimization example
  2. Jupyter Notebook: High level intro to Neural Networks in Tensorflow