Computer Science

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. 

In this course, students use probability theory to model uncertainty; design simple probabilistic models that facilitate prediction; conduct sound scientific analysis of data, and study the mathematical foundations of probabilistic modelling with Markov chains and simulation.

This course focuses on mobile robotics, emphasizing practical algorithms for navigation, all based around real hardware and tested in the real world. Key elements are: wheeled locomotion, motor control, and motion calibration; outward-looking sensors for behavioral control loops; probabilistic localization using particle filtering; advanced use of sensors for place recognition, occupancy mapping and planning; and an introduction to Simultaneous Localization and Mapping. The course is intensively practical, and all the key methods students learn are tested on robots they build.

This course provides an introduction to the foundations of 3D computer graphics. 

Students learn the basic methods used to define shapes, materials, and lighting when creating computer-generated images for use in film, games, and other applications. Topics include affine and projective transformations, clipping and windowing, visual perception, scene modeling and animation, algorithms for visible surface determination, reflection models, illumination algorithms, and color theory in depth. 

No official prerequisites, but the course assumes some programming experience in C or C++ and a basic knowledge of linear algebra. Exposure to calculus and image processing is useful but not required. 

The course's goal is to enable participants to acquire and process digital images in technical applications in a context-aware manner. The course introduces the basics of digital image processing, the acquisition of images in computing environments, and the extraction of semantic contents from the images. The goal of the course is the exemplary coverage of an interdisciplinary breadth, not necessarily an in-depth treatment of a specific domain. Fundamentals like sensor calibration, feature detection (e.g. edge extraction), matching and classification are taught. Integrated practical exercises cover operating a camera from a single-board computer and using a smartphone camera in a computer vision setting. Furthermore, exemplary machine learning approaches are used for “understanding” the images acquired previously. Software to be developed make use of the OpenCV Python library.

Data science has unlocked exciting possibilities for social scientists through its diverse toolkit, including big data analysis, visualisation, and machine learning models, enabling them to extract valuable insights from their data.  Yet, the success of a data-driven project hinges on data quality. This is where data engineering plays a pivotal role. Professionals must ensure that their acquired data is sufficient and accurate and must be adaptable to handle 'messy data' effectively. A substantial portion of time in data-driven projects (anecdotally 80%) is dedicated to cleaning and pre-processing data, with only 20% said to be devoted to building, evaluating, and deploying machine learning models. Despite the emergence of new AI technologies, which promise to automate many coding tasks, data manipulation is likely to remain an indispensable skill due to the inherent messiness of real-world data. By the end of this course, students will be proficient in producing a website to communicate your collected data and showcase your newly acquired data-wrangling abilities.

This introductory course provides a comprehensive introduction to digital research for students from a range of backgrounds. Through a variety of interactive sessions students develop an understanding of the key principles of Open Science and Scholarship, the importance of reproducibility and methods for managing research projects. The course serves as a platform for students to undertake digitally enabled research projects.

This course cover three important ideas in classical physics – Newton’s Laws of Motion, Newton’s Law of Gravitation and the Wave Equation. After considering analytical solutions to each, students look at computational solutions using the Python programming language (no background in coding is necessary) and touch on ideas such as dynamical systems and chaos. Students also look at solutions in different coordinate systems which give rise to familiar ideas such as Kepler’s laws of planetary motion and the inverse square law but from a first principles approach.