Instructor Information

  • Instructor: Matias G. Delgadino
  • Office: PMA 10.160
  • Office hours: Tuesday 9:00–10:00 AM, Wednesday 2:00–3:00 PM
  • Contact: matias.delgadino@utexas.edu
  • TA: Lukas Stefan Taus
  • Office hours: Monday & Thursday 1:00–2:00 PM
  • Contact: l.taus@utexas.edu

Course Meetings

  • Lectures: Tuesday & Thursday, 3:30–5:00 PM in PMA 5.122

Course Website

Course Description

This course introduces the mathematical foundations necessary for understanding and applying machine learning techniques. Emphasis is placed on optimization, linear algebra, and probability. Weekly Python homework assignments and three midterms are included.

Prerequisites

Basic knowledge of linear algebra, calculus, and probability.

Grading

  • Homework Assignments: 40%
  • Midterm Exams: 60%

Accommodations

Students with disabilities may request academic accommodations from the Division of Diversity and Community Engagement (DDCE), Services for Students with Disabilities (SSD).

Homeworks

  • J. Calder, P. J. Olver. Linear Algebra, Data Science, and Machine Learning, Springer 2025.
  • Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.
  • Gordan Zitkovic. Lecture notes for “Introduction to Stochastic Processes”. Link
  • Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.
  • Strang, G. (2016). Introduction to Linear Algebra. Wellesley-Cambridge Press.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Homeworks

Course Schedule

Date Lecture Topic Lecture Notes Suggested Reading
Tuesday, August 26 Introduction, Python Basics and Quiz Lecture Notes Syllabus, quiz
Thursday, August 28 Introduction to PCA and Compressed Sensing Lecture Notes Calder-Olver Chapter 2
Tuesday, September 2 Linear Algebra Review Lecture Notes Calder-Olver, Ch. 2–3
Thursday, September 4 Rank, Projections, Eigenvalues Lecture Notes Calder-Olver, Ch. 3
Tuesday, September 9 Matrix Decompositions Lecture Notes Calder-Olver, Ch. 5
Thursday, September 11 PCA Lecture Notes Calder-Olver, Ch. 8
Tuesday, September 16 Compressed Sensing Lecture Notes Shalev-Shwartz & Ben-David, Ch. 23
Wednesday, September 17 Last Drop Day
Thursday, September 18 Linear Regression I: Least Squares Lecture Notes Calder-Olver, Ch. 7
Tuesday, September 23 Linear Regression II: Non-linear Features Lecture Notes Calder-Olver, Ch. 7
Thursday, September 25 Supervised Learning and Regularization Lecture Notes Calder-Olver, Ch. 7
Tuesday, September 30 Midterm I Linear Algebra
Thursday, October 2 Optimization Basics: Calculus Review Lecture Notes Calder-Olver, Ch. 6
Tuesday, October 7 Convexity and Gradient Descent Lecture Notes Calder-Olver, Ch. 6
Thursday, October 9 Momentum Methods, Nesterov Acceleration Lecture Notes Boyd & Vandenberghe, Ch. 9
Tuesday, October 14 Support Vector Machines (SVM) Lecture Notes Calder-Olver, Ch. 6
Thursday, October 16 SVM: Dual Formulation Lecture Notes Calder-Olver, Ch. 6
Tuesday, October 21 SVM: Kernel Methods Lecture Notes Calder-Olver, Ch. 6
Thursday, October 23 Graphs: Adjacency Matrix Lecture Notes Calder-Olver, Ch. 9
Tuesday, October 28 Clustering: K-means Lecture Notes Calder-Olver, Ch. 9
Thursday, October 30 Midterm II Optimization and SVM
Tuesday, November 4 Clustering: Spectral Clustering Lecture Notes Calder-Olver, Ch. 9
Thursday, November 6 Random Walks on Graphs I Lecture Notes Zitkovic
Tuesday, November 11 Random Walks on Graphs II Lecture Notes Zitkovic
Thursday, November 13 Random Walks on Graphs III Lecture Notes Zitkovic
Tuesday, November 18 PageRank Lecture Notes Zitkovic
Thursday, November 20 Reversing a Random Walk Lecture Notes
Tuesday, November 25 Thanksgiving Break
Thursday, November 27 Thanksgiving Break
Tuesday, December 2 Generative Algorithms Lecture Notes
Thursday, December 4 Midterm III Clustering & Markov Chains