Advanced Models and Algorithms in Machine Learning
Knowledge in higher Mathematics including Analysis and Linear Algebra for Physicists, Mathematicians, Engineers and Computer Scientists. Knowledge in probabilistic data modelling and standard Machine Learning approaches.
In this seminar recent developments of models and algorithms in Machine Learning will be studied. Advances of established modelling approaches and new approaches will be presented and discussed along with the applications of different current algorithms to application domains including: auditory and visual signal enhancements, source separation, auditory and visual object learning and recognition, auditory scene analysis and inpainting. Furthermore, Machine Learning approaches as models for neural data processing will be discussed and related to current questions in Computational Neuroscience.
The students will learn about recent developments and state-of-the-art approaches in Machine Learning, and their applications to different data domains. By presenting scientific studies in the context of currently used models and their applications, they will learn to understand and communicate recent scientific results. The presentations will use computers and projectors. Programming examples and animations will be used to support the interactive component of the presentations. In scientific discussions of the presented and related work, the students will obtain knowledge about current limitations of Machine Learning approaches both on the theoretical side and on the side of their technical and practical realizations. Presentations of interdisciplinary research will enable the students to carry over their Machine Learning knowledge to address questions in other scientific domains.
- Pattern Recognition and Machine Learning, C. M. Bishop, Springer 2006.
- Information Theory, Inference, and Learning Algorithms, D. MacKay, Cambridge University Press, 2003. (online available)
- Machine Learning: A Probabilistic Perspective, K. P. Murphy, MIT Press, 2012.
- Standard Journals of the field.