Instructor: J. Lücke
Language: English
Time and place:

Wednesday: 14:15 - 15:45, W03 1-154

Knowledge in Machine Learning and their practical challenges for data modeling is required. Furthermore, knowledge in higher Mathematics including Analysis and Linear Algebra for Physicists, Mathematicians, Engineers and Computer Scientists.

Building up on advanced Machine Learning knowledge, this seminar discusses recent scientific contributions and developments in Machine Learning as well as recent papers on applications of Machine Learning algorithms. Typical application domains include general pattern recognition, computer hearing, computer vision and computational neuroscience. Typical tasks include auditory and visual signal enhancements, source separation, auditory and visual object learning and recognition, auditory scene analysis, data compression and inpainting. Applications to computational neuroscience will discuss recent papers on the probabilistic interpretation of neural learning and biological intelligence.

The students will learn the current research directions and challenges of the Machine Learning research field. By presenting examples from Machine Learning algorithms applied to sensory data tasks including task in Computer Hearing and Computer Vision the students will be taught the current strengths and weaknesses of different approaches. The presentations of current research papers by the participants will make use of 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 deepen their 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.

• Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, P. Dayan, L. F. Abbott, MIT Press, 2001.
• Standard ML Journals: JMLR, TPAMI, Neural Comp etc