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Current Trends in Machine Learning

WS 2012/13, TU München

Seminar

Location: Room 02.09.023
Date: Thursday, Presentations starting on 10.01.2013
Time: 14:00 - 16:00
Lecturer: Christina Lichtenthäler, Jan Stühmer, Jürgen Sturm
ECTS: 4
SWS: 2

Slides of the introductory meeting

The course will be held in English. The student can do his / her presentation in German, but English is prefered.

Inhalt

Maschinelles Lernen bezeichnet Methoden um Muster unter Nutzung von Vorwissen in strukturierten Daten aufzufinden. In diesem Seminar werden aktuelle Themen anhand von Konferenzbeiträgen aus dem Bereich Maschinelles Lernen behandelt. Dazu gehören Anwendungen in der Robotik, der Signalverarbeitung (z. B. blinde Quellentrennung), Empfehlungsdienste (Recommender Systems) und sich zeitlich verändernde Daten. Jeder Student wählt eine wissenschaftliche Veröffentlichung, die er im Seminar präsentiert.

Teilnehmerzahl: 12 Studenten (Master).
Registrierung: Per Email an Jan Stühmer.

Description Machine Learning studies computational methods that find patterns in structured data. In this seminar we will discuss current trends in machine learning based on research articles, including applications in robotics, signal processing (e.g. blind source separation), recommender systems and time varying data. Every student picks a recent research paper on machine learning that he presents in the seminar.

Attendance is limited to 12 students (Master).
Registrierung: Via Email to Jan Stühmer.

Important Dates
First Meeting Room 02.09.023, 16:00 - 18:00 18.10.2012 - mandatory - fixed assignment of topic and date
SeminarRoom 02.09.023, 14:00 - 16:00 10.01.2013, 17.01.2013, 24.01.2013, 31.01.2013


LaTeX-Template for the Report

A LaTeX-template for your seminar report is available for download here.

Paper Assigments
StudentDate of PresentationPublicationSupervisor
Daniele Casaburo 31.01.2013 Scholz,Klinkenberg, Boosting Classifiers for Drifting Concepts, 2007Jan Stühmer
Faegheh Nazari 24.1.2013 Chen et al., Marginalized Denoising Autoencoders for Domain Adaptation, 2012Jan Stühmer
Javad Fotouhi 17.01.2013 Rasmussen, Gaussian Processes in Machine Learning, 2004Jan Stühmer
Caner Hazirbas 10.01.2013 Nejigane et al., Online Action Recognition with Wrapped Boosting, 2007Christina Lichtenthäler
Julian Löchner 17.01.2013 Le et al.,Building High-level Features Using Large Scale Unsupervised Learning, ICML 2012Jürgen Sturm
Hessam Roodaki Lavasani 24.01.2013 Neher et al., Blind source separation techniques for the decomposition of multiply labeled fluorescence images, 2009 Jan Stühmer
Gennady Shabanov 17.01.2013 Rendle et al., BPR: Bayesian Personalized Ranking from Implicit Feedback, 2009Christina Lichtenthäler
Nikhil Somani 31.01.2013 Cao et al., Human motion recognition using support vector machines, 2009Christina Lichtenthäler
Vladyslav Usenko 10.01.2013 Shotton et al., Real-time human pose recognition in parts from single depth images, CVPR 2011Jürgen Sturm
Natalia Zarawska 10.01.2013 Rottmann et al., Semantic place classification of indoor environments with mobile robots using boosting, 2005Christina Lichtenthäler
Ilya Dianov 31.01.2013 Socher et al., Parsing Natural Scenes and Natural Language with Recursive Neural Networks, ICML 2011 Jan Stühmer
David Susanto 24.01.2013 Ziebart et al., Probabilistic Pointing Target Prediction via Inverse Optimal Control, IUI 2012 Jürgen Sturm
Saksham Gautam 31.01.2013 Wang et al., Graph-based Recommendation on Social Networks, 2010Christina Lichtenthäler


You can download the papers in PDF file format from this page (password needed). If you forgot the password, send a mail to Jan Stühmer.
If you have a specific interest concerning some particular paper or technique, that you would like to discuss in the seminar, contact Jan Stühmer at jan.stuehmer@in.tum.de.

Last edited 31.01.2013 13:33 by Jan Stühmer