Schedule

The class schedule below gives a rough idea of what will be covered in the course. It will be updated on a weekly basis. Numbers in the reading column refer to sections in the class notes:

Calder, J. & Olver, P.J. Linear Algebra, Machine Learning, and Data Science [PDF] (Updated 2023-05-08)

The notes will be updated frequently throughout the term, so please check back often. The class schedule includes links to the Python notebooks used in each class. When there are two notebooks for one class, the second notebook contains the solutions to the Python exercises from class.

Date Topic Reading Python Notes
Jan 18 Linear algebra and Python 1,2 .ipynb | .ipynb
Jan 23 QR Factorization and Python 2.5,3.8 .ipynb | .ipynb
Jan 25 Iterative Methods and Python 5.2,12.1 .ipynb | .ipynb HW1 Due
Jan 30 Least Squares and Python 5.2,5.3 .ipynb | .ipynb
Feb 1 Introduction to Machine Learning, Linear Regression 4.1,4.2 .ipynb
Feb 6 Linear Regression, Support Vector Machines 4.3,5.3 .ipynb
Feb 8 Support Vector Machines 4.3 .ipynb HW2 Due
Feb 13 K-Nearest neighbors, semi-supervised learning 4.4,4.6 .ipynb
Feb 15 k-Means Clustering 4.5 .ipynb
Feb 20 Gradient Descent 5.4,5.8,5.10 .ipynb
Feb 22 Gradient Descent 5.7,5.10 .ipynb
Feb 27 Gradient Descent 5.10 .ipynb HW3 Due (Feb 26)
Mar 1 Gradient Descent, Singular Value Decomposition 5.10, 6.5 .ipynb
Mar 6 Spring Break (No class)
Mar 8 Spring Break (No class)
Mar 13 Principal Component Analysis (PCA) 7.1,7.2,7.3 .ipynb
Mar 15 Principal Component Analysis (PCA) 7.3,7.4 .ipynb
Mar 20 Graph theory, binary spectral clustering 8.1,8.2,8.3,8.4 .ipynb
Mar 22 Diffusion on graphs 8.5 .ipynb HW4 Due
Mar 27 PageRank, spectral clustering 8.5,8.6 .ipynb
Mar 29 Graph-based semi-supervised learning 8.7 .ipynb
Apr 3 Difffusion maps and t-SNE embedding 9.1-9.4 .ipynb
Apr 5 Neural Networks 10.1-10.4 .ipynb HW5 Due
Apr 10 Neural Networks 10.1-10.4 .ipynb
Apr 12 Universal Approximation 10.5 .ipynb
Apr 17 Universal Approximation 10.5 .ipynb
Apr 19 Convolutional Neural Networks 10.6 .ipynb
Apr 24 Graph Neural Networks 10.7 .ipynb
Apr 26 Optimization: Stochastic Gradient Descent
May 1 Optimization: Continuum Perspective
May 10 Homework 6 Due HW6 Due
May 10 Projects Due