Moses - Machine Learning 1

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Module / Version:
#40550 / #6

Validity:
Seit SoSe 2020

Default display language:
English

Module title:
Machine Learning 1

Credits:
9

Responsible person:
Müller, Klaus-Robert

Grading:
benotet

Type of exam:
Schriftliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34352000 FG Maschinelles Lernen

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 4-1

Contact person:
Montavon, Gregoire

E-mail address:
klaus-robert.mueller@tu-berlin.de

Website:

https://wiki.ml.tu-berlin.de/wiki/

Learning Outcomes

The students are able to independently apply methods from machine learning on new data. This includes methods for classification, regression, dimensionality reduction and clustering. Moreover, the module teaches the mathematical skills (probability theory, optimization theory) needed to extend and theoretical analyze machine learning methods.

Content

Probability theory, theory of estimation (e.g. Maximum likelihood, EM algorithm) Methods from Machine Learning: Dimensionality reduction (PCA, ICA), Clustering, Supervised learning (e.g. Regression, LDA, SVM, Gaussian processes)

Module Components

Pflichtteil:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Maschinelles Lernen I	IV	0434 L 501	WiSe	English	6

Workload and Credit Points

Maschinelles Lernen I (IV):

Workload description	Multiplier	Hours	Total
			270.0h(~9 LP)
Concepts & Theory	15.0	6.0h	90.0h
Exercises	15.0	6.0h	90.0h
Programming	15.0	6.0h	90.0h

The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

weekly lectures, exercise sessions, and homeworks

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Basic knowledge in linear algebra and calculus. Basic knowledge in probability theory. Basic programming knowledge, programming in Python.

Mandatory requirements for the module test application:

1. Requirement

Unbenoteter Übungsschein

Module completion

Grading

graded

Type of exam

Written exam

Language

English

Duration/Extent

120 min

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Wintersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

cf. course webpage

Recommended literature
No recommended literature given

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
Biomedizinische Technik (M. Sc.)	1	9	SoSe 2021	SoSe 2023
Computational Engineering Science (Informationstechnik im Maschinenwesen) (M. Sc.)	1	7	SoSe 2020	SoSe 2023
Computer Engineering (M. Sc.)	1	42	SoSe 2020	SoSe 2023
Computer Science (Informatik) (M. Sc.)	1	35	SoSe 2020	SoSe 2023
Elektrotechnik (M. Sc.)	1	21	SoSe 2020	SoSe 2023
ICT Innovation (M. Sc.)	2	14	SoSe 2020	SoSe 2023
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)	2	13	SoSe 2020	SoSe 2023
Medieninformatik (M. Sc.)	1	14	SoSe 2020	SoSe 2023
Physikalische Ingenieurwissenschaft (M. Sc.)	2	15	SoSe 2020	SoSe 2023
Wirtschaftsingenieurwesen (B. Sc.)	1	8	SoSe 2020	SoSe 2023
Wirtschaftsingenieurwesen (M. Sc.)	1	24	SoSe 2020	SoSe 2023

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information

Machine Learning 1