HS2015

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.

The seminar will be held in Room 302 at Neubrückstrasse 10. This course will cover the basics on soft skills such as: critical analysis, scientific writing, presenting, peer reviewing, team work and program management. The course will use publications in computer vision and machine learning as examples to illustrate and practice each soft skill. Most of the work will be done and evaluated in class. Some sessions may be substituted by seminars.

This course is an introduction to different algorithms and evaluation methodology of ML

This seminar is dedicated to implement and evaluate different algorithms related to text classification and other NLP tasks

Proof theory is one of the four central pillars of mathematical logic, dealing with formal proofs, studying their properties and developing techniques for their representation and simplification. Proof theory and proof-theoretic techniques have important applications in computer science such as, for example, automated theorem proving, artificial intelligence, logical data bases and expert systems, formal verification. In this course we begin with classical first order logic the central and basic area for all sorts of reasoning, introduce the most important framework for formal proofs, and present the central normalization technique. Then we turn to the constructive variant of classical logic and end with the famous “proofs as computations” approach.

With this course, the students will get familiar with the basic notions and fundamental problems in combinatorial optimization and graph theory. They will learn how to use these theoretical problems to model real world problems as well as how to solve them. Also, they will see different types of algorithms and learn how to prove the correctness of an algorithm. In this course, we introduce some basic concepts and problems in combinatorial optimization, most of them being related to graph theory. In a first part, we recall the most important notions in graph theory and in computational complexity. Various types of algorithms are introduced and illustrated via concrete examples (like for instance exact algorithms, greedy algorithms, approximation algorithms, dynamic programming). In a second part, we focus on some well known problems in combinatorial optimization (like for instance minimum spanning tree, shortest path, network flow, graph coloring). We introduce the corresponding theory, analyse the algorithms and present some applications of these problems. The students will also learn how to model real world problems using these graph theoretical problems.