Hauptseminar Computational Aspects of Machine Learning - Winter 15

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Term
Winter 15
Lecturer
Kilian Röhner, Valeriy Khakhutskyy
Time and Place
Wed. 10am till 12pm room MI 02.07.023 starting from 21. October (see schedule below)
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
https://campus.tum.de/tumonline/lv.detail?clvnr=950213939



News

  • Due to the student body meeting the seminar on October 28th will start at 9 am.
  • Take a look at the Coursera Machine Learning course. If you are new to the field, the online course is a good way to get an overview.

Description

Machine Learning is a rapidly growing area of research on the intersection 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 embarrassingly 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 neighbors search or MCMC sampling.

Schedule

Preliminary schedule of the presentation

Date Forename Surname Topic Advisor Chair Reviewer 1 Reviewer 2
21.10.15 Alexander Prams Parallelisation paradigms and parallel performance models Valeriy Makan Hesam Felix
28.10.15 Sebastian Kreisel Data mining with sparse grids Kilian Hesam Martin Thomas
04.11.15 Claus Meschede Large scale Bayesian inference Valeriy Martin Alexander Makan
11.11.15 Aljaz Bozic Online learning Kilian Alexander Sebastian Hesam
18.11.15 Felix Sonntag Large scale kernel learning Valeriy Sebastian Makan Martin
25.11.15 Thomas Sennebogen Real-time data mining with guaranteed throughput Kilian Martin Aljaz Alexander
02.12.15 Makan Tayebi Gholamzadeh Resource-aware/constrained machine learning methods Emily Aljaz Felix Sebastian
09.12.15 Hesam Rabeti Large scale graph mining Kilian Felix Thomas Alexander Felix
16.12.15 Lilian Qian Machine learning as a black box solution
16.12.15 Martin Musiol Speeding-up deep learning Valeriy Thomas Sebastian Aljaz

Materials

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.

Application

If you feel like a data science unicorn we are looking for, please follow the link to application (was available from July 10th until July 15th, same period as the matching system) and send us the information about your background and experience, 3 preferred topic (ranked) or your own topic suggestions, as well as your expectations from the seminar.

Additionally, you are required to register for the seminar in the matching system.

Resources

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 max. 6 pages in IEEE format
  • Language: English
  • max. 10 Participants
  • Blind peer-review process
  • Session chairing

Dates

Dates to keep in mind:

  • July 10th till 15th: Application and registration in the matching system of the department
  • after July 23th: Notification of participants, topic discussions, etc.
  • Week starting with October 19th: first session, first talk.

Deadlines

  • 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