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| This seminar takes a closer look at classical topics in machine learning. | This seminar takes a closer look at classical topics in machine learning. "Classical Topics in Machine Learning" is an optional course in the module "Machine Learning 1" and is worth 3 LP (3 ECTS credits). In the general case, it is not possible to take the Python course as a standalone course. |
Block-Seminar "Classical Topics in Machine Learning"
Termine und Informationen
Kickoff Meeting |
Monday 27 November 2017 from 8am to 9am in room H 107 |
Presentation Days |
Thursday-Friday, 22-23 February 2018 from 9am to 7pm in room MAR 4.065 |
Dozenten: |
Stefan Chmiela chmidhkg@mailbox.tu-berlin.de |
|
Kristof Schütt kristof.schuett@tu-berlin.de |
Sprache |
Englisch |
Anrechenbarkeit |
Wahlpflicht LV im Modul Maschinelles Lernen I (Informatik M.Sc.) |
This seminar takes a closer look at classical topics in machine learning. "Classical Topics in Machine Learning" is an optional course in the module "Machine Learning 1" and is worth 3 LP (3 ECTS credits). In the general case, it is not possible to take the Python course as a standalone course.
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 20 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