Machine Learning

Seminar of the Machine Listening, Machine Vision and Models of Sensory Neuroscience

Organizers: Jörg Lücke, Jörn Anemüller, Timo Gerkmann, Bernd Meyer
Language: English
Time and place:  Thursday: 14:00 - 16:00, Room: W30 3-324 (NeSSy)

Content

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 computationalneuroscience will discuss recent papers on the probabilistic interpretation of neural learning and biological intelligence.

Schedule of the Machine Listening, Machine Vision and Models of Sensory Neuroscience Seminar Summer Term 2015

DateTitlePresenter
May 7thDictionary Learning with Occlusion and MasksJörg Lücke
May 21stConference Recaps (ICASSP 2015 and COSYNE 2015)Saboor, Raphael, ICASSP attendees
May 28thApproaches to Source Localization (acoustic, EEG, MEG)Hendrik and Cris
June­ 4thCollision with hearing aid developers forum
June 11thDeep classification on acoustic data/ECog dataBernd and Marina
June 18thT1: Human-level control through deep reinforcement learning, T2: Deep Learning for Acoustic ModellingAnnika and Constantin (maybe Dennis)
July 2ndSparse coding, NMF, dictionary learning, codebook learning, probabilistic approachesKamil, Jörg, Maryam, Georgios

July 9th

Signal enhancement and preprocessingTimo, Kamil
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Outcome

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 aboutcurrent 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.

Further Reading

  • Pattern Recognition and Machine Learning, C. M. Bishop, Springer, 2006. (best suited for lecture)
  • Machine Learning: A Probabilistic Perspective, K. P. Murphy, MIT Press, 2012
  • Information Theory, Inference, and Learning Algorithms, D. MacKay, Cambridge University Press, 2003. (free online)
  • Theoretical Neuroscience: Computational and Mathe - matical Modeling of Neural Systems, P. Dayan, L. F. Abbott, MIT Press, 2001
  • Standard ML Journals: JMLR, TPAMI, Neural Comp etc.