New curriculum
Generative AI Agents
Graduate lectures and projects on LLM reasoning, planning, tools, memory, robust agent design, and evaluation.
Teaching and curriculum
From machine learning foundations to generative AI agents.
Courses connect theoretical guarantees with hands-on systems for reasoning, planning, tool use, memory, and evaluation.
Summer 2026Generative AI Agents · DeepLearn 2026International school
Spring 2026Introduction to Machine Learning · CMSC 422Course site ↗
Fall 2025Generative AI Agents · CMSC 848NCourse site ↗
Spring 2024Introduction to Machine Learning · CMSC 422Course site ↗
Fall 2023Algorithms in Machine Learning · CMSC 742Course site ↗
Spring 2023Algorithms in Machine Learning · CMSC 742Course site ↗
Fall 2022Introduction to Machine Learning · CMSC 422Course site ↗
Spring 2022Introduction to Machine Learning · CMSC 422Course site ↗
Earlier teaching
Course archive, 2017–2020.
Earlier graduate and undergraduate courses preserve the theoretical foundations behind the lab’s current curriculum.
Fall 2020Algorithms in Machine Learning: Guarantees and Analyses · CMSC 828UCourse site ↗Lectures ↗
Spring 2019Algorithms in Machine Learning: Guarantees and Analyses · CMSC 828UCourse site ↗
Fall 2018Advanced Topics in Machine Learning · CMSC 498VCourse site ↗
Spring 2018Introduction to Machine Learning · CMSC 422Course site ↗
Fall 2017Machine Learning: Spectral Methods and Reinforcement Learning · CMSC 828RCourse site ↗
Graduate theory
Algorithms in Machine Learning
Methods with theoretical performance guarantees and applications across networks, biology, and language.
Undergraduate foundations
Introduction to Machine Learning
Learning theory, latent-variable models, and principled approaches to deep learning.