Moses - Beginners Workshop Machine Learning

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SS 2018 - WiSe 2023/24.

Module / Version:
#40897 / #1

Validity:
SS 2018 - WiSe 2023/24

Default display language:
English

Module title:
Beginners Workshop Machine Learning

Credits:
9

Responsible person:
Müller, Klaus-Robert

Grading:
benotet

Type of exam:
Mündliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34352000 FG Maschinelles Lernen

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 4-1

Contact person:
No information

E-mail address:
klaus-robert.mueller@tu-berlin.de

Website:

http://wiki.ml.tu-berlin.de

Learning Outcomes

The students have knowledge and practical experience with best practices in modern machine learning methods. They have essential and solid basic knowledge of current machine learning techniques and are able to apply them in practical scenarios. This includes conceptual steps for different problem settings as well as requirements and solutions for algorithmic and technical demands and competence in designing solutions independently.

Content

The goal of the workshop is to teach fundamental machine learning concepts and support the theory with practical exercises. Each day of the workshop will be subdivided into three parts. 1. Lecture about a particular concept or algorithm, e.g. cross-validation or SVMs 2. Instructed computer exercise session, where students get experience with the conveyed method 3. Homework on the topic to consolidate the daily learning progress

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Beginners Workshop Machine Learning	PJ		WiSe	No information	6

Workload and Credit Points

Beginners Workshop Machine Learning (PJ):

Workload description	Multiplier	Hours	Total
Attendance	15.0	6.0h	90.0h
Pre/post processing	15.0	12.0h	180.0h
			270.0h(~9 LP)

The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

Students are taught in daily lectures, each lecture covering one fundamental topic of modern machine learning practice. In a supervised exercise session, students are then able to gain experience with the implementation and application of the presented methods. There are assignments for every topic. Assignments are performed in groups of students.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

There are no formal prerequisites. Basic knowledge of linear algebra, statistics, calculus and programming is desirable.

Mandatory requirements for the module test application:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Oral exam

Language

English

Duration/Extent

20 minutes

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

The maximum capacity of students is 10.

Registration Procedures

Registration is done via e-mail. Registrations are treated in order of arrival. For details and dates see web site.

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.

Miscellaneous

No information

Beginners Workshop Machine Learning