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Machine Learning 1

9

English

#40550 / #6

Seit SoSe 2020

Fakultät IV

MAR 4-1

Institut für Softwaretechnik und Theoretische Informatik

34352000 FG Maschinelles Lernen

Müller, Klaus-Robert

Montavon, Gregoire

klaus-robert.mueller@tu-berlin.de

POS-Nummer PORD-Nummer Modultitel
2346664 38067 Machine Learning 1
61770 23151 Machine Learning 1

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 WS English 6

Workload and Credit Points

Maschinelles Lernen I (IV):

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

Verwendungen (4)
Studiengänge: 2 Stupos: 2 Erstes Semester: SoSe 2020 Letztes Semester: offen

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

Miscellaneous

No information