Mathematical Foundations of Machine Learning
Course details and planned materials for MATH 4990V and MATH 5990V.
Course Offering
Fall 2026
Course Description
This is an introductory course to machine learning (ML). ML is a powerful technique widely used in data-driven areas such as language processing, face recognition, image segmentation, banking, finance, insurance, drug discovery, and engineering.
We begin with Python, commonly used scientific packages, and standard datasets. Students are then introduced to a range of ML algorithms such as linear regression, logistic regression, k-means, k-nearest neighbors (KNN), support vector machines (SVM), naive Bayes, Hopfield networks, decision trees, random forests, gradient-boosted trees, deep neural networks, graph neural networks, and manifold learning techniques.
Planned Course Content
- Introduction to machine learning
- Data preprocessing
- Topological data analysis
- Linear regression
- Classification methods
- Clustering and manifold learning
- Neural networks and deep learning
Archive
Archived materials from the Spring 2024 offering are available here.