## Schedule

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)