Moses - Machine Intelligence I

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SS 2015 - WS 2016/17. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#40548 / #3

Validity:
SS 2015 - WS 2016/17

Default display language:
English

Module title:
Machine Intelligence I

Credits:
6

Responsible person:
Obermayer, Klaus

Grading:
benotet

Type of exam:
Schriftliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34351300 FG Neuronale Informationsverarbeitung

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 5-6

Contact person:
Obermayer, Klaus

E-mail address:
oby@ni.tu-berlin.de

Website:

https://www.tu.berlin/ni/studium-lehre/

Learning Outcomes

Participants should learn basic concepts, their theoretical foundation, and the most common algorithms used in machine learning and artificial intelligence. After completing the module, participants should understand strengths and limitations of the different paradigms, should be able to correctly and successfully apply methods and algorithms to real world problems, should be aware of performance criteria, and should be able to critically evaluate results obtained with those methods. More specifically, participants should be able to demonstrate 1) Knowledge of theory and methods of inductive learning 2) Application to problems of regression and classifcation (pattern recognition) 3) Understanding regarding basic concepts of neural information processing 4) Understanding regarding theoretical foundations to develop new machine learning techiques

Content

1) Foundations of inductive learning: empirical risk minimization, structural risk minimization, Bayesian inference 2) Learning and generalisation: gradient-based optimization, overfitting-underfitting, regularisation, application to regression and classification problems 3) Artificial neural networks (connectionist neurons, multilayer perceptrons, radial basis functions) 4) Statistical learning theory and support vector machines 5) Probabilistic methods and graphical models: reasoning under uncertainty, Bayesian learning for neural networks

Module Components

Pflicht:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Neuronale Informationsverarbeitung I	VL	0434L866	WiSe	No information	2
Neuronale Informationsverarbeitung I	UE	0434L866	WiSe	No information	2

Workload and Credit Points

Neuronale Informationsverarbeitung I (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)

Neuronale Informationsverarbeitung I (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: Besprechung von Übungsaufgaben, die von den Teilnehmern als Hausaufgaben bearbeitet werden. Die Übungsaufgaben umfassen sowohl die Analyse und Entwicklung von neuronalen Verfahren als auch den praktischen Umgang mit den besprochenen Verfahren. The lecture part consists of teaching in front of the class. Participants are expected to rehearse topics after class, using their class notes as well as recommended book chapters, in preparation for the exercises and tutorials. Homework assignments are given on a regular basis, and must be usually solved within a week. These assignments cover mathematical exercises as well as numerical simulations and programming exercises. Working in small groups of two to three students is encouraged. Homework assignments and their solutions are presented and discussed during the tutorial. In addition, selected topics presented during the lecture are rehearsed by the tutor as needed. The first tutorials cover a brief mathematics primer.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Gute Programmierkenntnisse, Grundkenntnisse in Linearer Algebra, Analysis und Wahrscheinlichkeitstheorie Mathematical knowledge: Analysis, linear algebra, probability calculus and statistics, on a level comparable to mathematics courses for engineers. Basic programming skills (Python, Matlab, or R). Good command of the English language.

Mandatory requirements for the module test application:

1. Requirement

[NI] Machine Intelligence I - Hausaufgabe

Module completion

Grading

graded

Type of exam

Written exam

Language

English

Duration/Extent

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

This module is not limited to a number of students.

Registration Procedures

Mündliche Prüfung: Voraussetzung ist das Vorliegen des unbenoteten Übungsscheins (mindestens 60% der Aufgaben müssen erfolgreich bearbeitet sein). Prüfungssprache (für die mündliche Prüfung) ist wahlweise Deutsch oder Englisch. Informationen zur Anmeldung sind über die Web-Seiten des Fachgebiets NI http://www.ni.tu-berlin.de/teaching/ und über das Sekretariat MAR 5042 erhältlich. At least 60% of all homework assignments have to be completed and an oral examination of up to one hour length has to be taken. The final grade is determined by the grade obtained in the oral examination. The oral exam can be taken in englisch or german. The oral exam has to be taken latest by the end of the semester which follows the semester in which the tutorial certificates were obtained. The examination procedure is regulated by the General Examination Regulation of the Technical University (Ordnung zur Regelung des allgemeinen Prüfungsverfahrens in Bachelor- und Masterstudiengängen) and by the Examination Regulation of the Master Program Computational Neuroscience. Further information regarding registration and course material are available via the webpages http://www.ni.tu-berlin.de/teaching/ and at the office MAR 5042.

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.

Masterstudiengang Informatik (Studienschwerpunkt Intelligente Systeme) Masterstudiengang Technische Informatik (StO/PO 2012): • Studienschwerpunkt Digitale Medien (Digital Media; Elektrotechnik oder Technische Informatik) • Studienschwerpunkt Kognitive Systeme (Cognitive Systems and Robotics; Informatik) • Studienschwerpunkt Mensch-Maschine-Interaktion und Design (Human-Computer Interaction and Design; Technische Informatik) • Studienschwerpunkt Datenanalyse (Data analytics; Informatik) • Studienschwerpunkt Informationssysteme Masterstudiengang Elektrotechnik (Ergänzungsmodul) Veranstaltung für andere Master-Studiengänge im Wahlbereich Bachelorstudiengang Technische Informatik (Studienschwerpunkt Datenanalyse) Bachelorstudiengang Wirtschaftsinformatik (Studienschwerpunkt Intelligente Datenanalyse )

Miscellaneous

Im Wintersemester 2016/2017 erfolgt eine durch den Prüfungsausschuss beschlossene einmalige Prüfungsformänderung von mündlich auf schriftlich.

Machine Intelligence I