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Probabilistic and Bayesian Modelling in Machine Learning and Artificial Intelligence - Seminar

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#40516 / #4

SS 2017 - WS 2017/18

English

Probabilistic and Bayesian Modelling in Machine Learning and Artificial Intelligence - Seminar

3

Opper, Manfred

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34351600 FG Künstliche Intelligenz

No information

Kontakt


MAR 4-2

Ruttor, Andreas

lehre@ki.tu-berlin.de

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

Learning Outcomes

Students have a profound knowledge of probabilistic models through independent work on a current field of research with the help of literature and independent elaboration of an example application. They are competent in presentating and explaining research topics in a talk.

Content

The seminar deals with current topics in the field of statistical modelling and inference. Example: application of Monte–Carlo-Methods on an inference problem.

Module Components

Pflichtteil:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Introduction to Computational GenomicsSEM0434 L 714SoSeNo information2

Workload and Credit Points

Introduction to Computational Genomics (SEM):

Workload descriptionMultiplierHoursTotal
Präsenzzeit15.02.0h30.0h
Vor-/Nachbereitung15.04.0h60.0h
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

Das Seminar beinhaltet das Einarbeiten in aktuelle Literatur und die Entwicklung eines Vortrages.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Vorausgesetzt werden Grundkenntnisse in Mathematik (Lineare Algebra, Analysis, Stochastik) sowie Programmierkenntnisse.

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

NamePointsCategorieDuration/Extent
(Ergebnisprüfung) Beispielanwendung40practical20 Minuten
(Ergebnisprüfung) Präsentation60oral45 Minuten

Grading scale

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl1.01.31.72.02.32.73.03.33.74.0
100.0pt95.0pt90.0pt85.0pt80.0pt75.0pt70.0pt65.0pt60.0pt55.0pt50.0pt

Test description (Module completion)

Die Gesamtnote gemäß § 47 (2) AllgStuPO wird nach dem Notenschlüssel 2 der Fakultät IV ermittelt.

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 12.

Registration Procedures

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

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
Information Theory, Inference, and Learning Algorithms, David J C MacKay, Cambridge University Press.
Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006.

Assigned Degree Programs


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

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
This module is not used in any degree program.

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

Service-Veranstaltung für andere Studiengänge (vor allem aus dem natur- und ingenieurwissenschaftlichen Bereich und der Mathematik und Statistik)

Miscellaneous

No information