Linear Algebra
Theory and Applications
A modern introduction to fundamental linear algebra with rigorous mathematical training and motivating examples from data science. The course emphasizes both how methods work and why the underlying ideas matter.
Advanced Machine Learning
Theory, Algorithms, and Applications
Advanced methods in ensemble learning, representation learning, generative models, graph neural networks, causal inference, and trustworthy machine learning, with emphasis on theory and modern applications.
Online Decision-Making
Algorithms, Learning, and Games
Advanced theory and applications of online algorithms, including regret and competitive analysis, multi-armed bandits, online convex optimization, no-regret learning in games, and algorithms with predictions.
Introduction to Computer Science
Programming Methodology
An introduction to programming with Python, modern computer systems, problem solving, algorithm design, functions, input and output, data structures, and object-oriented programming.
Python Programming
Data Science Foundations
Practical Python for data science, including core data structures, NumPy, exploratory analysis with Pandas, predictive models, visualization with Matplotlib, and an implementation project.