Moses - Computational Learning Theory

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum WiSe 2020/21 - SoSe 2021.

Module / Version:
#41014 / #1

Validity:
WiSe 2020/21 - SoSe 2021

Default display language:
English

Module title:
Computational Learning Theory

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 and especially statistical background of machine learning. This is achieved by the independent study of a current research topic in the field including rigorous mathematical derivations using literature and by the independent preparation of a seminar presentation.

Content

The seminar deals with the theory of machine learning from a (mathematically) rigorous statistical perspective. The so-called PAC (probably almost correct) learning framework focusses on the question how well a machine learning algorithm which was trained on random independent data can make predictions on new instances which are generated from the same but unknown probability distribution. The seminar will be based on the recent textbook “Understanding Machine Learning-From theory to Algorithms” by Shai Shalev-Schwartz and Shai Ben-David. After explaining the statistical learning framework, the seminar will discuss uniform convergence results and the VC dimension. It will also discuss theoretical aspects of different machine learning models, such as convex learning, support vector machines, boosting, online learning and stochastic gradient descent. It will cover regularisation, model selection and dimensionality reduction. Finally, also more advanced topics such as Rademacher complexities will be included.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Computational Learning Theory	SEM	3435 L 10792	WiSe	English	2

Workload and Credit Points

Computational Learning Theory (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) presentation	50	oral	45 minutes
(Learning process review) consultation	50	oral	20 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:
Wintersemester.

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
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
This module is not used in any degree program.

Miscellaneous