Computer Science

Students are introduced to data science and its practice: how it works and how it can produce insights from social, political, and economic data. It combines accessible knowledge of data science as a field of study with practical knowledge about data science as a career path. By combining case studies in applications of both with the study of the content of data science, it covers data science that is both pedagogic but accessible, as well as fundamentally applied and practical. The course combines three perspectives: inferential thinking, computational thinking, and real-world relevance.

In this course, students gain an integrative understanding of the field of Artificial Intelligence (AI), with equal emphasis on data-driven AI (especially machine learning) and model-based AI (especially planning and reasoning). They come to understand AI from the perspectives of decision theory, machine learning, optimization, and classical problem solving. Students learn to independently implement and understand core algorithms from these areas and can identify appropriate problem formulations and AI algorithms for a given application. Course topics include problem formulations and algorithmic approaches from decision theory (including reinforcement learning, multi-armed bandits, control theory), machine learning, optimization, and inference, classical planning, and problem solving. The class also discusses fundamental and recurring algorithmic principles such as dynamic programming, optimization-based vs. sampling-based methods, and decision trees.

 

This course teaches students to evaluate geometric machine learning as a tool to model common learning frameworks. Students design optimizers on Riemannian manifolds to implement smooth constrained optimization; synthesize discrete operators on graphs from their continuous versions; and modify learning models to operate on constrained domains and outcomes. As part of the course, students implement deep learning on unstructured domains such as graphs, point sets, and meshes, as well as mechanisms to yield structured output from learning models.

This course's aim is to develop an understanding of large language models (LLM) as well as their evaluation and to practice reading, understanding, and presenting research work. As part of the class, methods for the selection of data and LLMs, data preparation, application and evaluation of LLMs are developed and put into practice. The use cases are examples from the field of natural language processing, e.g. translation, summary of text, or information extraction.

This advanced topics course covers reinforcement learning, search, and test-time scaling of large language models that are expected to drive the next generation of AI systems. 

Topics include: Basics of RL (Markov Decision Process and Policy evaluation), Basics RL (Imitation learning, Deep policy gradient methods), Basics of RL (Deep Q-Learning, Rainbow DQN); Symmetric alternating Markov games, Monte Carlo tree search, expert iteration, and AlphaGo; Imperfect information games, Counerfactural regret minimization, and Pluribus; NLP basics (RNN, beam search, tokenizers); NLP basics (Transformers, encoder-decoder architectures); Instruction fine-tuning, Scaling laws of LLM pre-training; Reinforcement learning with human feedback, direct policy optimization, Group Relative Policy Optimization (GRPO); Chain of thought, Process reward models, Prover-verifier games; In-context learning, Scaling LLM Test-Time Compute; DeepSeek-R1. 

This advanced undergraduate course delves deeply into Internet technology. It covers the structure of the Internet and its protocol applications in detail. Students examine the basic design principles, implementation, and operating principles of computer networks used in modern Internet and cloud/data centers, and study in detail the design principles and functions of the transport layer, network layer, link layer, and physical layer, including client-server models, web, video streaming, and smart phone network applications. If time permits, the course includes ultra-low latency/ultra-bandwidth networking issues in data centers. An understanding of the OSI protocol and basic concepts of data communication is required. 

All engineering disciplines today employ machine learning for monitoring systems and fault detection, for data-based decision support as well as for leveraging new potentials in the environment of big data. This module teaches the fundamentals of standard machine learning techniques as well as their implementation using standard libraries in the Python programming language based on real-world engineering examples. It focuses on the complete data science process from data exploration over modeling to inference and production.

The course is an introduction to the geometry of the image formation process and how visual data is represented and manipulated in a computer. Students learn projective geometry, which helps model the perspective projection, and digital image processing. Topics include how to model the perspective operation that happens when a picture is taken (projective geometry, image formation process), how pictures (visual data) are represented and processed in a computer (digital image processing), how to find out the internal geometric parameters of a camera (camera calibration), and what applications camera technology has in robotics (stereopsis, visual odometry, AR/VR, etc.).

The course provides an overview of data center technologies, the infrastructure needed to run a variety of workloads, and the design decisions when engineering scalable distributed applications. Students analyze the full system stack for managing and scheduling data-center resources. Further, they discuss the design principles for scalable systems; investigate concepts and techniques to build large scale systems, with a focus on distributed storage, coordination, computation and resource allocation. They get an overview of NewSQL and NoSQL technologies, learn new data models, their associated query languages and systems, and discuss new storage technology and its impact on query execution and data management systems in general.

Data science and machine learning are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. This course extends the foundation of probability and statistics with an introduction to the most important concepts in applied machine learning, with social science examples. It covers the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods covered.