The class schedule below will be updated on a weekly basis. Numbers in the reading column refer to sections in the class notes:

Calder, J. Mathematics of Image and Data Analysis [PDF] (Updated 2022-05-10)

The notes will be updated frequently throughout the term, so please check back often. Note: Some web browsers will cache the notes and display an older version. Check the date on the title page of the notes against the date above. If the date above is more recent, then clear your browser cache, or access the page from a private browsing window.

Date Topic Reading Python Slides
Jan 19 Introduction to the course and Python 1.1,1.2 .ipynb .pdf
Jan 24 Linear algebra review and Python 2.1,2.2 .ipynb .pdf
Jan 26 Linear algebra review and Python 2.3,2.4 .ipynb .pdf
Jan 28 Homework 1 Due
Jan 31 Principal Component Analysis (PCA) 3.1,3.2,3.3 .ipynb .pdf
Feb 2 Principal Component Analysis (PCA) 3.5,3.6 .ipynb .pdf
Feb 7 K-Means Clustering 4.1 .ipynb .pdf
Feb 9 Spectral Clustering 4.2 .ipynb .pdf
Feb 11 Project 1 Due
Feb 14 PageRank 5.1,5.2 .ipynb .pdf
Feb 16 Gradient descent 9.1 .ipynb .pdf
Feb 21 Newton’s Method 9.2 .ipynb .pdf
Feb 23 Introduction to the DFT 6.1,6.2 .ipynb .pdf
Feb 25 Homework 2 Due
Feb 28 The Fast Fourier Transform (FFT) 6.3 .ipynb .pdf
Mar 2 Parseval’s Identities and Convolution 6.4,6.5 .ipynb .pdf
Mar 7 Spring Break (No class)
Mar 9 Spring Break (No class)
Mar 14 Signal Denoising (Tikhonov) 6.6.1 .ipynb .pdf
Mar 16 Signal Denoising (Total Variation) 6.6.2 .ipynb .pdf
Mar 21 Multidimensional DFT and Image denoising 6.7 .ipynb .pdf
Mar 23 Discrete Cosine Transform and Sampling Theorem 6.8,6.9 .ipynb .pdf
Mar 25 Homework 3 Due
Mar 28 The Haar Wavelet 7.1,7.2,7.3,7.4 .ipynb .pdf
Mar 30 Introduction to Machine Learning 8.1 .ipynb .pdf
April 4 Graph-based semi-supervised learning 8.2 .ipynb .pdf
April 6 Graph-based embeddings (spectral, t-SNE) 8.3 .ipynb .pdf
April 8 Project 2 Due
April 11 Neural Networks (Back propagation) 8.4.1,8.4.2 .ipynb .pdf
April 13 Convolutional Neural Networks 8.4.3, 8.4.5 .ipynb .pdf
April 18 Stochastic Gradient Descent 9.1.5 .ipynb .pdf
April 20 Momentum Descent 9.1.3 .ipynb .pdf
April 25 Nesterov’s Accelerated Gradient Descent 9.1.4 .ipynb .pdf
April 27 Universal Approximation 8.4.4 .pdf
May 2 Autoencoders .ipynb .pdf
May 2 Neural Style Transfer .ipynb
May 6 Project 3 Due
May 6 Homework 4 Due
May 11 Final exam (take-home)