- CS 598 (Spring
2024): Principles of
Generative AI:
Recent advancements in generative AI have equipped machine learning algorithms with the ability to learn from and accurately replicate observed data, creating new, similar data instances. This course provides an in-depth exploration of the key algorithmic developments in generative models, together with their underlying mathematical principles. We will cover a range of topics such as normalizing flows, variational autoencoders, Langevin algorithms, generative adversarial networks, diffusion models, and sequence generation models, etc.
- CS 598 (Fall
2024): Machine Learning Algorithms for Large Language Models:
This course is a general overview of machine learning algorithms
used in the current development of large language models
(LLMs). It covers a relatively broad range of topics, starting
with mathematical models for sequence generation, and important
neural network architectures with a focus on transformers. We will
then investigate variants of transformer based language models,
along with algorithms for prompt engineering and improving
reasoning capability. Other topics include ML techniques used in
studying LLM safety, hallucination, fine-tuning of LLMs, alignment
(reinforcement learning from human feedback), multimodal LLMs, and
common methods for accelerating training and inference.
- CS 446 (Spring 2025): Machine Learning:
The goal of machine learning is to develop algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed for a particular task. In this course, we will cover the common algorithms and models encountered in both traditional machine learning and modern deep learning, those in unsupervised learning, supervised learning, and reinforcement learning. The algorithms that we will cover include k-means, Gaussian mixture models, expectation maximization, decision trees, Naive Bayes, linear regression, logistic regression, support vector machines, kernel methods, boosting, learning theory, common neural network architectures including FCN, CNN, RNN, LSTM, Transformer, and training algorithms, basic reinforcement learning algorithms such as Q-learning and policy gradient.
- CS 540 (Fall 2025): Deep Learning Theory:
This course rigorously covers foundational concepts in learning theory, emphasizing theoretical analysis related to modern deep learning frameworks. Key topics include generalization analysis, VC-dimension, covering numbers, Rademacher complexity, stochastic gradient descent, common techniques for lower bound analysis, universal approximation results, Neural Tangent Kernel (NTK) regime optimization, benign overfitting, and mean-field analysis. Evaluation consists of four homework assignments, a take-home midterm exam, and a group final project. The course aims to equip students with the theoretical foundations necessary to engage with current research literature.
- CS 498 (Spring 2026): Introduction to Generative AI:
This course provides an introduction to modern machine learning techniques for developing and applying generative models, focusing on
how to model and generate language, image, and multimodal data. Topics include autoregressive models and large language models, representation
learning methods, diffusion and related generative methods, multimodal generation and understanding, and post-training and alignment. Students
will learn how these systems are trained, how they generate new outputs, and how they can be used in practice. The course emphasizes
both the theoretical foundations and hands-on implementation.