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| ||'''Presentation Days'''|| Monday-Wednesday, 13.-15.01.2019, 09:00-17:00in room MAR 4.065|| | ||'''Presentation Days'''|| Monday-Wednesday, 13.-15.01.2019, 09:00-17:00 in room MAR 4.065|| |
Block-Seminar "Classical Topics in Machine Learning"
Termine und Informationen
Kickoff Meeting |
Monday 11.11.2019 from 8:15 - 9:15 in room MA 042 (math building) |
Presentation Days |
Monday-Wednesday, 13.-15.01.2019, 09:00-17:00 in room MAR 4.065 |
Dozent: |
Dr. Marina Höhne (née Vidovic) marina.hoehne@tu-berlin.de |
Sprache |
Englisch |
Anrechenbarkeit |
Wahlpflicht LV im Modul Maschinelles Lernen I (Informatik M.Sc.) |
ISIS (2019) |
!!! Important !!!
1. The seminar is limited to 30 Students
2. If you take the ML1-X Module: This seminar is (an optional) part of the ML1-X Module and you can take the seminar. Alternative seminar options for the ML1-X Module are the big data seminar/data management seminar.
3. If you take the Cognitive Algorithm Module: This seminar is part of the Cognitive Algorithm Module and you can take the seminar.
4. If you don’t take the ML1-X Module or the Cognitive Algorithm Module: You have to check with your examination office if you can count the ECTS credits and get a written form. Note that the students attending the ML1-X Module and the Cognitive Algorithm Module have priority if there are too many participants.
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 "Cognitive Algorithm" and is worth 3 LP (3 ECTS credits). There is no formal registration for the kick-off meeting. In the general case, it is not possible to take the seminar 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
Deep Learning
Interpretation
To find a list of all optional papers, follow the link to the ISIS page: https://isis.tu-berlin.de/course/view.php?id=15302