HS2022

In this seminar, we will investigate hot topics in the domain of operating systems, encompassing computer architecture, hardware and emerging technologies, storage systems, and networking. At the kick-off sessions, we will present the scope of the seminar and given an overview of the domain. During the seminar, students will be assigned individual projects, which include reading and presenting state-of-the-art research papers. Regular meetings will be scheduled during the semester.

This course covers fundamental topics in computer vision. The course will provide an introduction to image formation, image processing, feature detection, segmentation, multiple view geometry and 3D reconstruction, motion, object recognition and classification.

This course aims at providing an overview and understanding of recent research in machine learning, computer vision, and artificial intelligence.

This course considers modeling and optimization aspects of mixed-integer linear programming (or integer programming for short). This important subdomain of mathematical programming and extension of linear programming considers the problem of optimizing a linear function of many variables, some or all of them restricted to be integers, subject to linear constraints. Integer programming is a thriving area of optimization. It has countless applications in production planning and scheduling, logistics, layout planning and revenue management, to name just a few. Thanks to effective and reliable software, it is widely applied in industry to improve decision-making. In this course, we cover the theory and practice of integer programming. In the first part, we address mathematical modeling aspects. We discuss how integer variables can be used to model various practically relevant, complex decision problems. We then introduce some standard optimization problems and develop, analyze and compare different integer programming formulations for them. We also introduce powerful modeling and solving tools and test them on the optimization problems given in the course. In the second part, we address optimization aspects, in which we discuss the basic methodology applied to solve integer programs. In particular, we consider implicit enumeration techniques (branch and bound), polyhedral theory, cutting planes and primal heuristics. We also look at some advanced techniques, such as Danzig-Wolfe decomposition and column generation.

Introduction to some basic concepts and notions of graph theory. Presentation of a series of graph theoretical problems (vertex coloring, edge coloring, maximum matching, ...) which have real world applications (in sports scheduling, timetabling, transmission problems... ), and learn how these problems can be handled using graph theoretical techniques. Introduction of special graph classes and their properties. Learn how to model other practical problems using the graph theoretical notions introduced.

This course explains the basic concepts of algorithmic privacy and fairness. The main application area is statistics and machine learning.

This course looks at recent research in privacy and fairness in machine learning from a theoretical, algorithm and legal perspective. It is best taken in parallel with the basic course on fairness and privacy.