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#40894 / #2

WS 2018/19 - SoSe 2020

English

Mathematics of Machine Learning

6

Stanczak, Slawomir

benotet

Mündliche Prüfung

Zugehörigkeit


Fakultät IV

Institut für Telekommunikationssysteme

34331800 FG Netzwerk- und Informationstheorie

No information

Kontakt


HFT 6

Reinhardt, Kerstin

sekretariat@netit.tu-berlin.de

PORD-Nr.ModultitelLPBenotungPrüfungsformPNr. (POS)Modulprüfung PORDModulprüfung PNr.
39602

Learning Outcomes

After completeing the module the students will have a solid understanding of theoret- ical foundations of Machine Learning and will be able to develop, apply, and analyze the complexity of the resulting learning algorithms. Moreover, a special emphasis will be put on applications of Machine Learning in areas such as Signal Processing and Wireless Communications and the students will be able to theoretically analyze and algorithmically solve learning problems arising in these fields.

Content

The learning content includes: • Learning Model • Learning via Uniform Convergence • Bias-Complexity Tradeoff • Stochastic Inequalities and Concentration of Measure • Suprema of empirical Processes • Vapnik- Chervonenkis Dimension (VC Dimension) • Nonuniform Learning • Runtime of Learning • Hilbert Spaces and Projection Methods • Kernel and Multi-Kernel Methods • Information Innovation • Regularization, Dimension Reduction and Compressive Sensing

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Mathematical Introduction to Machine LearningVLWSNo information2
Theory and Algorithms of Machine Learning for CommunicationVLSSEnglish2

Workload and Credit Points

Mathematical Introduction to Machine Learning (VL):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h

Theory and Algorithms of Machine Learning for Communication (VL):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The module consists of conventional frontal teaching in class, developing theoretical and mathematical concepts, exercises developed in class, in order to develop problem- solving skills and reinforce comprehension of the theory,

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Prerequisite for participation to courses are a mathematical background at the level of beginning MS students in Electrical Engineering (multivariate calculus, signals and systems, linear algebra and notions of matrix theory). The course is open to students enrolled in any MSc in EE CS, Mathematics and Physics.

Mandatory requirements for the module test application:

No information

Module completion

Grading

graded

Type of exam

Oral exam

Language

English

Duration/Extent

45 minutes

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:
Winter- und Sommersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

Course teaching and organization (not module examination enrollment at Examination office/Prüfungsamt) is supported by an ISIS course. Registration details are provided at the beginning of the module.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  available
Additional information:
Will be provided during the course

 

Literature

Recommended literature
P. Rigollet: Mathematics of Machine Learning, MIT Lecture Notes (online)
R. Vershynin: High-Dimensional Probability: An Introduction with Applications in Data Sciences (book in preparation, online)
S. Shalev-Schwartz and S. Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014

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.

Miscellaneous

No information