||The focus of this course, apart from providing the theoretical basis of pattern analysis and recognition, is to demonstrate the tools and methodologies required for applying the received knowledge in real-life problems, and build up experience in a practical application sense. The course will introduce the basics of a number of themes in different detail. A subset of these themes will be worked further through the development of project work. This structure allows the course to cover the issue of pattern recognition in considerable breadth through the lectures, introducing a number of concepts and methodologies, while at the same time it allows the students to acquire more in-depth experience with a subset of the themes examined through the practical work. In parallel, the course demands the students to work both autonomously to learn and expand their knowledge on the basis of the material introduced, as well as in teams to develop the practical work required. In particular the following objectives are set for students attending this course: To develop a scientific way of thinking and acquire critical reasoning skills To develop their teamwork skills and work cooperatively in a group To develop their individual learning skills To build a good understanding of basic data analysis concepts such as normalization, regularization, probability, clustering. 1 Pattern Analysis and Recognition 2015 - 2016 To have a working knowledge of various methodologies for regression and classification, including Bayesian Classification, Support Vector Machines, Nearest Neighbour Search, and Decision Trees. To acquire basic knowledge of structural pattern recognition, graph based methods, neural networks and graphical models. To build the ability to develop and evaluate pattern recognition systems apply them to solving real-world problems.