Courses
STOR 565 -- Machine Learning (Fall 2026)
Course Design and Outline

Course Design and Outline

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

Visit the Fall 2026 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 Fall 2026 course website; sign-in and course access may be required. Submissions and grades are provided through Canvas.

Policies and evaluation

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

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