Video Link (Click Lect. #) Date Topics
0 9 January 2020 Introduction (PDF)
1 14 January 2020 Overview of Machine Learning and Imaging (PDF)
2 16 January 2020 Continuous Mathematics Review (PDF)
3 16 January 2020 From Continuous to Discrete Mathematics (PDF)
4 21 January 2020 Discrete Functions (PDF)
5 23 January 2020 Introduction to Optimization (PDF)
6 29 January 2020 Ingredients for Machine Learning (PDF)
7 31 January 2020 Linear and Logistic Classification (PDF)
8 4 February 2020 “Deep” Networks: theoretical motvation (PDF)
9 6,11 February 2020 Convolutional Neural Networks (PDF)
10 11,13 February 2020 Backpropagation in Deep Networks (PDF)
11 18 February 2020 Tools for your Deep Learning Toolbox (PDF)
12 20 February 2020 CNN implementation and visualization (PDF)
13 25 February 2020 CNN visualization tools and extensions (PDF)
14a 27 February 2020 CNNs for object detection and segmentation(PDF)
14b 3 March 2020 CNNs as Autoencoders (PDF)
15 5 March 2020 Introduction to Physical Layers in Machine Learning (PDF)
16 24 March 2020 Introduction to Fourier Optics (PDF)
17 26 March 2020 Physical Layers with Coherent Fields (PDF)

Lecture Resources

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