Schedule

Note: This website is from Spring 2021. The current (Spring 2022) course is here.

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]

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.

Lecture Date Topic Reading Python Slides
1 Jan 20 Introduction to the course and Python 1.1,1.2 .ipynb .pdf
2 Jan 25 Linear algebra review and Python 2.1,2.2 .ipynb .pdf
3 Jan 27 Linear algebra review and Python 2.3,2.4 .ipynb .pdf
Jan 29 Homework 1 Due
4 Feb 1 Principal Component Analysis (PCA) 3.1,3.2,3.3 .ipynb .pdf
5 Feb 3 Principal Component Analysis (PCA) 3.4,3.5 .ipynb .pdf
6 Feb 8 K-Means Clustering 4.1 .ipynb .pdf
7 Feb 10 Spectral Clustering 4.2 .ipynb .pdf
Feb 12 Project 1 Due
8 Feb 15 PageRank 5.1,5.2 .ipynb .pdf
9 Feb 17 Introduction to the DFT 6.1,6.2 .ipynb .pdf
10 Feb 22 The Fast Fourier Transform (FFT) 6.3 .ipynb .pdf
11 Feb 24 Parseval’s Identities and Convolution 6.4,6.5 .ipynb .pdf
Feb 26 Homework 2 Due
12 Mar 1 Signal Denoising (Tikhonov) 6.6.1 .ipynb .pdf
13 Mar 3 Signal Denoising (Total Variation) 6.6.2 .ipynb .pdf
14 Mar 8 Signal Denoising (Total Variation) 6.6.2 .ipynb .pdf
15 Mar 10 Multidimensional DFT and Image denoising 6.7 .ipynb .pdf
Mar 12 Homework 3 Due
16 Mar 15 Discrete Cosine Transform and Sampling Theorem 6.8,6.9 .ipynb .pdf
17 Mar 17 The Haar Wavelet 7.1,7.2 .ipynb .pdf
18 Mar 22 Denoising, compression, classification 7.2,7.3 .ipynb .pdf
19 Mar 24 Wavelets & Intro to Machine Learning 7.4,8.1 .ipynb .pdf
Mar 26 Project 2 Due
20 Mar 29 Graph-based semi-supervised learning 8.2 .ipynb .pdf
21 Mar 31 Graph-based embeddings (spectral, t-SNE) 8.3 .ipynb .pdf
April 5 Spring Break (No class)
April 7 Spring Break (No class)
22 April 12 Graph-based embeddings (spectral, t-SNE) 8.3 .ipynb .pdf
23 April 14 Neural Networks (Back propagation) 8.4.1,8.4.2 .ipynb .pdf
24 April 19 Classification with neural networks 8.4.3 .ipynb .pdf
25 April 21 Universal Approximation 8.4.4, 8.4.5 .ipynb .pdf
26 April 26 Convolutional Neural Networks 8.4.5 .ipynb .pdf
27 April 28 Gradient descent 9.1,9.2 .pdf
April 30 Homework 4 Due
28 May 3 Gradient descent: Momentum 9.3 .pdf
May 6-7 Final exam (take-home)
May 12 Project 3 Due