Video Date Topics
  7 Jan 2026 Course Introduction (PDF)
Lecture 1 Video 12 Jan 2026 Machine Learning and Imaging Introduction (PDF)
Lecture 2 Video 12 Jan 2026 Mathematical Preliminaries Part I (PDF)
Lecture 3 Video 14 Jan 2026 Mathematical Preliminaries Part II (PDF)
Lecture 3 Video 14 Jan 2026 Fourier Transforms and Sampling (PDF)
Lecture 4 Video 21 Jan 2026 Sampling and Discrete Math (PDF)
Lecture 5 Video 28 Jan 2026 Introduction to Optimization (PDF)
Lecture 6 Video 2 Feb 2026 Ingredients for Machine Learning (PDF)
Lecture 7 Video 4 Feb 2026 Ingredients for Machine Learning, Part 2 (PDF)
Lecture 8 Video 9 Feb 2026 Gradient Descent and Beyond Linear Classification (PDF)
Lecture 9 Video 11 Feb 2026 Theoretical basics of machine learning (PDF)
Lecture 10 Part 1 Video 16 Feb 2026 Ingredients for CNN’s, Part I (PDF)
Lecture 10 Part 2 Video 18 Feb 2026 Ingredients for CNN’s, Part II (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
  4. Online learning and stochastic approximation Link

Jupyter Notebook Examples

  1. Jupyter Notebook: PyTorch basic optimization example
  2. Jupyter Notebook: High level intro to Neural Networks in PyTorch
  3. Jupyter Notebook: Weighted image sum example - Associated cube1.mat datafile
  4. Jupyter Notebook: Physical layers example
  5. Jupyter Notebook: Spectral Un-mixing
  6. Jupyter Notebook: A simple Autoencoder in Tensorflow/Keras
  7. Jupyter Notebook: GAN example