Provides a solid understanding of the fundamental concepts of probability theory and statistics. The course covers the basic probabilistic concepts such as probability space, random variables and vectors, expectation, covariance, correlation, probability distribution functions, etc. Important classes of discrete and continuous random variables, their inter-relation, and relevance to applications are discussed. Conditional probabilities, the Bayes formula, and properties of jointly distributed random variables are covered. Limit theorems, which investigate the behavior of a sum of a large number of random variables, are discussed. The main concepts random processes are then introduced. The latter part of the course concerns the basic problems of mathematical statistics, in particular, point and interval estimation and hypothesis testing.

An introduction to the data science pipeline, i.e., the end-to-end process of going from unstructured, messy data to knowledge and actionable insights. Provides a broad overview of what data science means and systems and tools commonly used for data science, and illustrates the principles of data science through several case studies.

This course covers the principles and key techniques of building and deploying large language models as well as multimodal foundation models (e.g., for images, audio, and video). Topics include Transformer architectures, multimodal representation learning, and diffusion-based generative models. The course also covers post-training and alignment (e.g., supervised fine-tuning, reinforcement learning), grounding with retrieval and tools, rigorous evaluation, and efficient deployment.

This course explores agentic AI concepts, architectures, workflows, and tooling. Topics include agentic prompting, state management and memory, multi-step reasoning and planning, agent orchestration, and evaluation. Students will gain hands-on experience with key concepts through course projects.