Over the past years, there has been a growing interest in Unsupervised Learning methods, mainly due to the massive increase in the volume and types of data stored, as well as, in their use for Big Data Analytics. Since tagging such quantities of data infeasible, various methodologies have been developed to allow systems to exploit these large amounts of data and the information they provide in a non-supervised manner.
The third annual Unsupervised Machine Learning Seminar will be the satellite event for the 2017 Speech Processing Conference. The focus of the seminar will be theory and application of unsupervised learning mechanisms such as clustering and deep learning in various domains. The keynote lecture will be given by Prof. Shai Ben-David who will speak about a satisfactory theory of clustering - an interesting question in this field. The event will include several short lectures.
The seminar will be held immediately following and is included in the registration fee for the conference. Registration for the satellite event is also available as a separate fee.
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Program: Satellite Event - Unsupervised Machine Learning Seminar
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| 16:30 - 16:45 |
Welcome Remarks
- Dr. Itshak Lapidot, Seminar Chairman, Afeka
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| 16:45-17:45 |
Keynote
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Prof. Shai Ben-David
School of Computer Science
University of Waterloo, Canada
“How far are we from having a satisfactory theory of clustering”
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| 17:45-18:45 |
Short Lectures:
- "Diffusion methods for aligning medical datasets: location prediction in CT scan images"
Neta Rabin, Unit of Mathematics, Afeka
- "Intra-Cluster training strategy for deep learning with applications to language identification"
Alan Bekker, Faculty of Engineering, Bar-Ilan University
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"Learning human behaviors and lifestyle by capturing temporal relations in mobility patterns"
Mr. Eyal Ben Zion, Department of Industrial Engineering and Management, Faculty of Engineering, Ben-Gurion University
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