Teaching

CS 4824 - Machine Learning

Undergraduate course, Virginia Tech, Fall 2025

Taught By: Dr. Dawei Zhou

This course provides a comprehensive introduction to machine learning, covering both the theoretical foundations and practical applications. Introduces core concepts such as statistical learning, supervised and unsupervised methods, graphical models, and deep learning architectures including CNNs, RNNs, and transformers. The course emphasizes hands-on implementation and application of algorithms to real-world problems in vision, language, and beyond. Advanced topics like generative AI, GANs, and diffusion models are also explored.

CS 5805 - Machine Learning

Graduate course, Virginia Tech, Spring 2025

Taught By: Dr. Naren Ramakrishnan

This course provides an introduction to the field of machine learning (or data mining) and explores preprocessing, classification, clustering, the discovery of association rules/sequential atterns, and anomaly detection. Introduces fundamentals of probability theory and random variables for classification and clustering. Investigates multiple linear and nonlinear regression models to classify social phenomena. Apply application of machine learning in solving real work problems.

CS 4804 - Introduction to AI

Undergraduate course, Virginia Tech, Fall 2024

Taught By: Dr. Yinlin Chen

This course introduces the foundations of modern artificial intelligence (AI) and key ideas and techniques underlying the design of intelligent computer systems. It comprises practice effective methods of reasoning about AI problems, which will generalize beyond the specific topics we study in class. Topics include (but are not limited to) search, game playing, logic, machine learning, deep learning, natural language processing, robotics and image processing.

DS 2000 - Programming with Data

Undergraduate Course, Northeastern University, Fall 2020, 2021

Introduces programming for data and information science through case studies in business, sports, education, social science, economics, and the natural world. Presents key concepts in programming, data structures, and data analysis through Python. Explains the data analytics pipeline and prepares students to work with data-driven projects.