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.

Programming, software and hardware design and implementation issues of computing systems for machine learning. Topics in the programming/software domain will include: basic Python program structure, variables and assignment, built-in data types, flow control, functions and modules; basic I/O, and file operations. Classes, object-oriented programming and exceptions. Regular expressions, database access, network programming and sockets. Introduction to the Numpy, Scipy and Matplotlib libraries. Topics in the hardware domain include computer architecture, CPUs, single- and multi-core architectures, GPUs, memory and I/O systems, persistent storage, and virtual memory. Parallel processing architectures, multiprocessing and cluster processing.

This course will cover a broad range of issues in developing human-centered AI with a focus on participatory approaches. We will look at approaches to building AI systems that expand human capabilities, and the interplay between human and AI skills. We will explore how to make use of expertise in those communities impacted by AI systems to design them better. Topics include the fundamentals of HCI and AI, interpretability and explainability in machine learning, human-centered design for AI, adaptive user interfaces, and conversational agents. The course will teach students to design machine learning systems that are well integrated with human capabilities and concerns.

In this course, we will discuss ideas of safe and trustworthy AI from a socio-technical and ethical point of view. We will familiarize ourselves with concepts and approaches from various disciplines, including social sciences and humanities, and dive into the literature on trust, safety and trustworthiness. We will then use these perspectives to examine key (technical) aspects of AI technologies. These include for example privacy, security, bias and reliability. The aim is to develop skills and approaches to reflect on the interplay of social and technological aspects of AI technologies through a critical but constructive lens to develop them in a safe and trustworthy manner.

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.