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#40834 / #4

Seit SoSe 2020

English

Machine Learning 1-X

12

Müller, Klaus-Robert

benotet

Schriftliche Prüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34352000 FG Maschinelles Lernen

No information

Kontakt


MAR 4-1

Montavon, Gregoire

klaus-robert.mueller@tu-berlin.de

PORD-Nr.ModultitelLPBenotungPrüfungsformPNr. (POS)Modulprüfung PORDModulprüfung PNr.
38164
42084

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), Clustering, Supervised learning (e.g. Regression, LDA, SVM, Gaussian processes) Depending on the elective: more detailed knowledge about specific machine learning problems, programming skills, or mathematical foundations.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Maschinelles Lernen IIV0434 L 501WSEnglish6

Wahlpflicht:

Please choose courses with 3 credit(s) from the following courses.

Course NameTypeNumberCycleLanguageSWSVZ
Algorithms for brain reading and writingSEMWSNo information2
Bayesian LearningVL343 L 8043WSNo information2
Big Data: Skalierbares Maschinelles LernenKUWSNo information2
Big Data: Skalierbares Maschinelles LernenSEMWSNo information2
Boosting and Model AveragingSEMWS/SSNo information2
Classical Topics in MLSEM0434 L 588WSNo information2
Deep Neural NetworksVLWS/SSNo information2
Machine Learning and Data Management SystemsSEMWS/SSNo information2
Machine Learning in the SciencesVLSSNo information2
Mathematische Grundlagen für Maschinelles LernenKU0434 L 545WS/SSNo information2
Matlabprogrammierung für ML und DatenanlyseKU0434 L 544WS/SSNo information2
Pythonprogrammierung für ML und DatenanlyseKU0434 L 543WS/SSNo information2
Scientific applications in Machine LearningSEMWS/SSNo information2

Workload and Credit Points

Maschinelles Lernen I (IV):

Workload descriptionMultiplierHoursTotal
270.0h(~9 LP)
Concepts & Theory15.06.0h90.0h
Exercises15.06.0h90.0h
Programming15.06.0h90.0h

Algorithms for brain reading and writing (SEM):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h

Bayesian Learning (VL):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
No information15.02.0h30.0h
No information15.04.0h60.0h

Big Data: Skalierbares Maschinelles Lernen (KU):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h

Big Data: Skalierbares Maschinelles Lernen (SEM):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h

Boosting and Model Averaging (SEM):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h

Classical Topics in ML (SEM):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
No information15.02.0h30.0h
No information15.04.0h60.0h

Deep Neural Networks (VL):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h

Machine Learning and Data Management Systems (SEM):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
No information15.04.0h60.0h
No information15.02.0h30.0h

Machine Learning in the Sciences (VL):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h

Mathematische Grundlagen für Maschinelles Lernen (KU):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
No information15.02.0h30.0h
No information15.04.0h60.0h

Matlabprogrammierung für ML und Datenanlyse (KU):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
No information15.02.0h30.0h
No information15.04.0h60.0h

Pythonprogrammierung für ML und Datenanlyse (KU):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
No information15.02.0h30.0h
No information15.04.0h60.0h

Scientific applications in Machine Learning (SEM):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h
The Workload of the module sums up to 360.0 Hours. Therefore the module contains 12 Credits.

Description of Teaching and Learning Methods

weekly lectures, tutorials, 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
Machine Learning 1-X: Übungsschein Wahlpflichtveranstaltung bestanden

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:
2 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

cf. course webpage

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
No recommended literature given

Assigned Degree Programs


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

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Biomedizinische Technik (M. Sc.)28SoSe 2021WiSe 2022/23
Computational Engineering Science (Informationstechnik im Maschinenwesen) (M. Sc.)24SoSe 2022WiSe 2022/23
Computer Engineering (M. Sc.)136SoSe 2020WiSe 2022/23
Computer Science (Informatik) (M. Sc.)130SoSe 2020WiSe 2022/23
Elektrotechnik (M. Sc.)118SoSe 2020WiSe 2022/23
ICT Innovation (M. Sc.)26SoSe 2020WiSe 2022/23
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)212SoSe 2020WiSe 2022/23
Physikalische Ingenieurwissenschaft (M. Sc.)26WiSe 2021/22WiSe 2022/23
Wirtschaftsingenieurwesen (M. Sc.)115SoSe 2020WiSe 2022/23

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

Miscellaneous

No information