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| The first meeting for the classical topics in ML will take place during the first week of november. | == Block-Seminar "Classical Topics in Machine Learning" == === Termine und Informationen === ||'''Erster Termin für Themenvergabe'''|| Thursday November 5, 10:00 in MAR 4.033 || ||'''Termin für Präsentation'''|| TBD || ||'''Verantwortlich'''|| [[http://www.ml.tu-berlin.de/menue/members/klaus-robert_mueller/|Prof. Dr. Klaus-Robert Müller]] || ||'''Dozent:''' || Pieter-Jan kindermans p.kindermans@tu-berlin.de || ||'''Sprache'''|| Englisch || ||'''Anrechenbarkeit''|| Wahlpflicht LV im Modul Maschinelles Lernen I (Informatik M.Sc.) || ||'''ISIS'''|| || This seminar takes a closer look at classical topics in machine learning. Students will read, understand, evaluate and present selected research papers on machine learning methods in different applications settings. At the end of the semester, each student will present his/her topic in a 25-30 min talk (+ questions) in English. The topics of the seminar are: * '''Boosting''' * '''Neural Networks''' * '''Feature Selection''' * '''Optimization Algorithms''' * '''Independent Component Analysis''' * '''Structured Prediction''' * '''Kernel Methods''' * '''Support Vector Machines''' * '''Gaussian Processes''' * '''Learning Theory''' * '''Multi-Task Learning''' * '''Robust Parameter Estimation''' |
Block-Seminar "Classical Topics in Machine Learning"
Termine und Informationen
Erster Termin für Themenvergabe |
Thursday November 5, 10:00 in MAR 4.033 |
Termin für Präsentation |
TBD |
Verantwortlich |
|
Dozent: |
Pieter-Jan kindermans p.kindermans@tu-berlin.de |
Sprache |
Englisch |
Anrechenbarkeit |
Wahlpflicht LV im Modul Maschinelles Lernen I (Informatik M.Sc.) |
ISIS |
|
This seminar takes a closer look at classical topics in machine learning.
Students will read, understand, evaluate and present selected research papers on machine learning methods in different applications settings. At the end of the semester, each student will present his/her topic in a 25-30 min talk (+ questions) in English.
The topics of the seminar are:
Boosting
Neural Networks
Feature Selection
Optimization Algorithms
Independent Component Analysis
Structured Prediction
Kernel Methods
Support Vector Machines
Gaussian Processes
Learning Theory
Multi-Task Learning
Robust Parameter Estimation