Moses - Monte Carlo Methods in Machine Learning and Artificial Intelligence

Display language

To modulepage Generate PDF

Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SS 2017 - WS 2017/18.

Module / Version:
#40584 / #4

Validity:
SS 2017 - WS 2017/18

Default display language:
English

Module title:
Monte Carlo Methods in Machine Learning and Artificial Intelligence

Credits:
6

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:
No information

E-mail address:
lehre@ki.tu-berlin.de

Website:

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

Learning Outcomes

The students get to know new methods and current applications in artificial intelligence and machine learning. They know and understand the current literature in this field and are able to present their knowledge in a talk.

Content

Monte Carlo simulation plays an prominent role in statistics, machine learning and statistical physics. This lecture will give an overview of classical and more recent methods and their applications. Topics will include rejection and importance sampling, sequential Monte Carlo methods (particle filters) and Markov chain Monte Carlo techniques such as the Gibbs sampler, the Metropolis Hastings method, exact sampling and Hamiltonian Monte Carlo. Assessment will be by homework and mini projects including short presentations of methods and results. The lecture should be of interest to students in computer science, mathematics, statistics, physics and engineering.

Module Components

Pflichtteil:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Monte Carlo Methods in AI and ML	VL	3435 L 721	SoSe	No information	2
Monte Carlo Methods in AI and ML	UE	3435 L 719	SoSe	No information	2

Workload and Credit Points

Monte Carlo Methods in AI and ML (VL):

Workload description	Multiplier	Hours	Total
Präsenzzeit	15.0	2.0h	30.0h
Vor-/Nachbereitung	15.0	2.0h	30.0h
			60.0h(~2 LP)

Monte Carlo Methods in AI and ML (UE):

Workload description	Multiplier	Hours	Total
Präsenzzeit	15.0	2.0h	30.0h
Vor-/Nachbereitung	15.0	6.0h	90.0h
			120.0h(~4 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

Vorlesung: Frontalunterricht vor allen Teilnehmern zur Vermittlung des Stoffes Übungen: Bearbeitung von kleineren Projekten, Präsentation der Resultate in kurzen Vorträgen.

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

Name	Points	Categorie	Duration/Extent
(Ergebnisprüfung) Darstellung (Vortragsgestaltung)	30	oral	30 Minuten
(Ergebnisprüfung) Inhalt des Vortrags (Theorie und Resultate der Projekte)	70	oral	1Stunde

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)

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:
2 Semester.

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

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

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

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.

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)

Miscellaneous

No information

Monte Carlo Methods in Machine Learning and Artificial Intelligence