Foundations to Machine Learning
CS/Math 350, Spring 2025
This course explores the process of machine learning through the lens of empirical modeling. We will
develop the theory and algorithms that underpin the process of learning interesting things about data.
Algorithms we will develop typically include: singular value decomposition and eigenfaces, the n-armed
bandit, projections and linear regression, data clustering (k-means, Neural Gas, Kohonen's SOM),
linear neural networks, optimization algorithms, autoencoders and deep networks. The course will
involve some computer programming, so previous programming experience is helpful. May be elected
as Computer Science 350. Prerequisite: Mathematics 240.
There is no required text for this course. Class notes will be provided.