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Machine Intelligence I

6

English

#40548 / #4

SS 2017 - WS 2017/18

Fakultät IV

MAR 5-6

Institut für Softwaretechnik und Theoretische Informatik

34351300 FG Neuronale Informationsverarbeitung

Obermayer, Klaus

Obermayer, Klaus

oby@ni.tu-berlin.de

POS-Nummer PORD-Nummer Modultitel
2345252 35096 Machine Intelligence I

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, deep networks) 4) Statistical learning theory and support vector machines 5) Probabilistic methods and graphical models: reasoning under uncertainty, Bayesian learning for neural networks 6) Reinforcement Learning (MDP, value iteration, policy iteration, Q-learning)

Module Components

Pflicht:

All Courses are mandatory.

Course Name Type Number Cycle Language SWS
Machine Intelligence I VL 0434 L 866 WS No information 2
Machine Intelligence I UE 0434 L 866 WS No information 2

Workload and Credit Points

Machine Intelligence I (VL):

Workload description Multiplier Hours Total
Präsenzzeit / time of attendance 15.0 2.0h 30.0h
Vor- und Nachbereitung / preparation and review 15.0 2.0h 30.0h
60.0h(~2 LP)

Machine Intelligence I (UE):

Workload description Multiplier Hours Total
Hausaufgaben / homework 15.0 6.0h 90.0h
Päsenzzeit / time of attendance 15.0 2.0h 30.0h
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 ssignments cover mathematical exercises as well as numerical simulations and programming exercises. orking in small groups of two to three students is encouraged. Homework assignments and their solutions are resented and discussed during the tutorial. In addition, selected topics presented during the lecture are ehearsed 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:

No information

Module completion

Grading

graded

Type of exam

Written exam

Language

English

Duration/Extent

90 min.

Duration of the Module

This module can be completed in one semester.

Maximum Number of Participants

The maximum capacity of students is 80.

Registration Procedures

Die Anmeldung zur schriftliche Prüfung erfolgt über Moses. Prüfungssprache ist Englisch. Beide Wiederholungsprüfungen werden als mündliche Prüfungen abgelegt. 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. The final grade is determined by the grade obtained in the written exam, which is held in English. Both potential re-examinations are oral exams. Other than that, 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 reading, Lecture notes

Lecture notes

Availability:  unavailable

Electronical lecture notes

Availability:  available

Literature

Recommended literature
No recommended literature given.

Assigned Degree Programs

Miscellaneous

No information