HS2023

Today's computing systems (e.g., Deep learning systems), become ever more complex, due to the rapid development of hardware and software technology. It is challenging to design and run computing systems that guarantee users' performance requirements in a resource-efficient way. Various quantitative methods are applied to capture such complex system dynamics and predict metrics of interests, from the designing phase of the systems to the runtime performance, e.g., job response times and system anomalies. To optimize the performance of computing systems, a deep understanding of those methods and their applications on the system design is essential. Having practical hand-on experience on designing experiments, deriving models, and validating results with benchmark systems will prepare students to tackle challenges of real-world systems. Course topics include Design of experiments and statistical tests. Operational laws and queueing methods for modelling computing systems. Scheduling and load balancing. Machine learning methods for modelling computing systems. System dependability and scalability analysis. Optimization and resource management.

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.

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

This course gives an introduction to the theory of machine learning. Application is in the form of a course project. During the course, you will be able to: - Formulate machine learning problems in terms of opimisation or probabilistic inference. - Apply off-the-shelf algorithms to problems. - Develop custom models and algorithms using the TensorFlow python library.

This course will give an overview of the theory and practice of fairness and privacy from an algorithmic perspective, applied to problems in machine learning. - Fairness concepts: Meritocracy, Discrimination - Privacy concepts: Anonymity, Differential Privacy

- Introduction of 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... ) - 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.