Course Design and Outline

Tue, 11 Aug 2026 00:00:00 +0000

STOR 565 (Fall 2026)

Machine Learning

Course overview

This course provides a mathematically rigorous introduction to the core ideas of statistical machine learning for advanced undergraduate and master’s students. It emphasizes foundational principles rather than a broad survey of algorithms, using representative models to illustrate the central ideas behind modern machine learning.

The course balances theoretical understanding with practical implementation. Students will learn why machine learning methods work, what assumptions they rely on, and how foundational ideas connect to modern applications.

Course website

for current materials, activities, and course information. This page summarizes the course’s learning goals and design.

Learning goals

Build a unified understanding of the main machine learning paradigms.
Understand the mathematical principles underlying core ML methods.
Implement and analyze representative machine learning models.
Connect foundational ideas to modern applications such as representation learning and language models.
Independently evaluate new advances and emerging AI techniques.

Course structure

1. Foundations of machine learning

Supervised, unsupervised, and online learning
Empirical risk minimization and generalization
Evaluation metrics and validation

2. Learning with labels

Classification and regression
Logistic regression as a probabilistic model
Convex optimization and gradient-based methods
Regularization and the bias–variance trade-off
Support vector machines and kernel methods

3. Learning without labels

Clustering and dimensionality reduction
Matrix factorization and representation learning
Generative models and probabilistic modeling
Foundations of language models

4. Learning in the loop

Interactive and sequential learning
Online learning and feedback-driven ML systems
Preference learning and reinforcement-learning-inspired ideas

Course design

This is a theory-oriented machine learning course with a substantial practical component. Students should expect mathematical analysis, implementation-based assignments, work with real datasets, and a final project connecting theory to modern ML practice.

Tools and materials

Programming: Python
Writing: LaTeX (Overleaf recommended)
Materials: Open-source readings, lecture slides and notes, and lab sessions

Course materials and activities are available through the ; sign-in and course access may be required. Submissions and grades are provided through .

Policies and evaluation

For the current schedule, evaluation details, and course policies, consult the course website and Canvas.

STOR 565 -- Machine Learning (Fall 2026) |