Moses - Theoretical Foundations of Machine Learning

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SoSe 2020 - SoSe 2021.

Module / Version:
#40977 / #1

Validity:
SoSe 2020 - SoSe 2021

Default display language:
English

Module title:
Theoretical Foundations of Machine Learning

Credits:
3

Responsible person:
Opper, Manfred

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34351600 FG Künstliche Intelligenz

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 4-2

Contact person:
Lippke, Cordula

E-mail address:
manfred.opper@tu-berlin.de

Website:

http://www.ki.tu-berlin.de/menue/lehre

Learning Outcomes

Students have a deep and profound understanding of the theoretical foundations of machine learning. This is achieved by the independent study of a current research topic in the field using literature and the independent preparation of an application study. They are also able to present scientific topics in a talk.

Content

The seminar deals with the theoretical foundations for the development, the analysis and the understanding of machine learning algorithms. Topics include convex learning problems, stochastic gradient descent, regularisation, online learning and many more. It will based on the recent textbook “Understanding Machine Learning-From theory to Algorithms” by Shai Shalev-Schwartz and Shai Ben-David.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Theoretical Foundations of Machine Learning	SEM	3435 L 10531	SoSe	English	2

Workload and Credit Points

Theoretical Foundations of Machine Learning (SEM):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Pre/post processing	15.0	4.0h	60.0h
			90.0h(~3 LP)

The Workload of the module sums up to 90.0 Hours. Therefore the module contains 3 Credits.

Description of Teaching and Learning Methods

The seminar includes skill adaptation training in current literature as well as the development of a talk.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Requirements: good knowledge in Mathematics (linear algebra, analysis, stochastics) as well as programming experience.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

English

Test elements

Name	Points	Categorie	Duration/Extent
(Deliverable Assessment) Application	50	practical	45 minutes
(Deliverable assessment) Presentation	50	oral	45 minutes

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	95.0pt	90.0pt	85.0pt	80.0pt	75.0pt	70.0pt	65.0pt	60.0pt	55.0pt	50.0pt

Test description (Module completion)

No information

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:
Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 16.

Registration Procedures

Die Anmeldung zur Lehrveranstaltung erfolgt über die ISIS-Seite. Dies ersetzt nicht die Prüfungsanmeldung in QISPOS bzw. beim Prüfungsamt.

Recommended literature
Understanding Machine Learning- From theory to Algorithms” by Shai Shalev-Schwartz and Shai Ben-David

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
This module is not used in any degree program.

Miscellaneous