Hauptseminar Computational Aspects of Machine Learning - Winter 14

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Term
Winter 14
Lecturer
Emily Mo-Hellenbrand, Kilian Röhner, Valeriy Khakhutskyy
Time and Place
Preliminary meeting: June 26th 4pm, Room MI 01.07.023
Audience
Students from Bachelor {Informatics, Information Systems, Games Engineering} (IN0014), Master {Informatics, Games Engineering} (IN2107) and Master Computational Science and Engineering (IN2183)
Tutorials
-
Exam
-
Semesterwochenstunden / ECTS Credits
2 SWS / 4 Credits
TUMonline
tba https://campus.tum.de/tumonline/lv.detail?clvnr=950163961



News

  • The Schedule is now set and online.
  • A new Coursera Machine Learning course started recently. If you are new to the field, the online course is a good way to get an overview.
  • You can find the slides of the preliminary meeting here

Description

Machine Learning is a rapidly growing area of research on the intersaction of applied mathematics, informatics, and computational science. With advances in the machine learning theory and algorithms as well es with increasing amount of data and complexity of the models, development of fast, efficient and scalable algorithms increasingly gains importance. Hereby the range of applied techniques spreads from exploiting embarasingly parallel tasks in a data-centric fashion to approximation methods with satisfying error limits.

In the seminar we are going to focus on the advanced methods for machine learning with particular interest in handling large-scale problems. While some topics would deal with the complete learning algorithms, others would focus on efficient solution of subtasks common for many different algorithms, e.g. nearest neighbours search or MCMC sampling.

Schedule

Date of Presentation First Name Name Topic Advisor Session Chair Reviewer 1 Reviewer 2
22. Oktober Haris Jabbar Parallelisation paradigms and parallel performance models Valeriy
29. Oktober Karthikeya Sampa Subbarao Map-reduce for machine learning algorithms Valeriy Aiham Aleksandar Shpend
5. November Michael Lettrich Data Mining with sparse grids Kilian Denys Gunnar Aiham
12. November Grant Henry Bartel Approximate k-nearest neighbors search Emily Aleksandar Karthikeya Denys
19. November Hannah Winnes Random projection matrices, feature hashing Valeriy Gunnar Michael Aleksandar
26. November Shpend Mahmuti Advanced MCMC algorithms Emily Karthikeya Grant Gunnar
3. Dezember Aiham Taleb Large scale Bayesian inference Emily Michael Hannah Karthikeya
10. Dezember Denys Sobchyshak Online learning Kilian Grant Shpend Michael
17. Dezember Aleksandar Bojchevski Real-time data mining with guaranteed throughput Kilian Hannah Aiham Grant
17. Dezember Gunnar König Speeding-up deep learning Valeriy Shpend Denys Hannah

Materials

The materials for the course are made available for the participants in a password protected section.

Prerequisites

The seminar is intended for computer science bachelor students in advanced semesters and the master students with focus on computer science, computational science and engineering, or robotics and artificial intelligence.

If you want to participate, you are expected to have knowledge of fundamentals of applied mathematics, such as linear algebra, probability theory, calculus, and convex optimisation. As we will cover advanced topics, some general understanding of the basic concepts of machine learning is required.

Moreover, you should have programming experience in at least one programming language. Understanding of basic concepts of computational science is desirable, so if you know what SIMD, MISD, NUMA, MPI, GPGPU, and HDF5 mean, you are on the safe side.

Organisational Details

  • Weekly sessions. Day, time, and room tba.
  • Talk: 45 minutes + 15 minutes discussion
  • Extended abstract: 1 page article document class with motivation, key concepts and results
  • Paper: min. 5 pages in IEEE format
  • Language: English
  • max. 10 Participants
  • Blind peer-review process

Dealines

Dates to keep in mind:

  • July 4th till 8th: Application and registration in the matching system of the department
  • after July 12th: Notification of participants, topic discussions, etc.
  • Week starting with October 20th: first session, first talk.

Dealines:

  • 1 week before the talk: submission of an extended abstract
  • The days of the talk: submission of a preliminary paper for review
  • 1 week after the talk: receiving comments from reviewers
  • 2 week after the talk: submission of the final paper

LaTeX