Display language
To modulepage Generate PDF

#40653 / #5

Seit WS 2019/20

English

Machine Learning Project

9

Müller, Klaus-Robert

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34352000 FG Maschinelles Lernen

No information

Kontakt


MAR 4-1

No information

No information

Learning Outcomes

The students have knowledge about and practical experience in independent application of machine learning methods to a real world dataset in a specific application scenario. This particularly includes pre-processing of real world data, calibration of prediction methods, and the comparison of different approaches. The students are also able to apply machine learning methods to other real world datasets, as well as estimating the extent, complexity, and chance of success of such a project from a practical point of view.

Content

The project's goal is the development of a prediction process (regression/classification) for a real world application, based on a open-source machine learning toolbox. A real world dataset in raw format is given. The project is subdivided into three milestones that are based on another. 1. Extraction of feature vectors from raw data; univariate and multivariate evaluation of these features. 2. Evaluation and comparison of different prediction methods; development of appropriate assessment approaches and quality criterions. 3. Justified selection and final assessment of a specific prediction method. Compared to the Lab Course this module does not focus on implementation of machine learning methods, but the data pre-processing as well as application, evaluation, and selection of methods from existing toolboxes.

Module Components

Pflichtteil:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Projekt Maschinelles LernenPJ0434 L552WiSeNo information6

Workload and Credit Points

Projekt Maschinelles Lernen (PJ):

Workload descriptionMultiplierHoursTotal
Präsenzzeit15.06.0h90.0h
Vor-/Nachbereitung15.012.0h180.0h
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 work in groups of two. There will be a meeting after each milestone where students present and discuss their results (seminar) and the goals for the next milestone are presented (lecture). Further, there will be group meetings at a regular basis.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

There are no formal prerequisites. The lecture "Machine Learning I" and the "Lab Course Machine Learning" are recommended. Programming will be done in Python.

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

NamePointsCategorieDuration/Extent
Programmcode/Dokumentation 1. Meilenstein33written1/3
Programmcode/Dokumentation 2. Meilenstein33written1/3
Programmcode/Dokumentation 3. Meilenstein34written1/3

Grading scale

This exam uses its own grading scale (see test description).

Test description (Module completion)

After each milestone, participants submit program code and documentation of their solution. There are strict guidelines for the program code as well as for the documentation of the solution which will be published on the website. There are three milestones and 1/3 of the evaluation of each milestone is included in the grade. The solutions are made available to the other participants so that they can build on them in the following milestones.

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 20.

Registration Procedures

The registration takes place by email. Registrations will be considered in the order in which they are received. For details and deadlines, see ML chair wiki.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
Christopher M. Bishop (2006) Pattern Recognition And Machine Learning , Springer.
Homepage der Machine Learning Toolbox scikit-learn: http://scikit-learn.org/
Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern Classification , Wiley (2. Auflage).
Trevor Hastie, Robert Tibshirani, Jerome Friedman (2001) The Elements of Statistical Learning, Springer.

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)130WS 2019/20SoSe 2024
Computer Science (Informatik) (M. Sc.)130WS 2019/20SoSe 2024
Elektrotechnik (M. Sc.)120WS 2019/20SoSe 2024
Information Systems Management (Wirtschaftsinformatik) (M. Sc.)110WS 2019/20SoSe 2024
Medieninformatik (M. Sc.)110WS 2019/20SoSe 2024
Medientechnik (M. Sc.)14WiSe 2023/24SoSe 2024
Wirtschaftsingenieurwesen (M. Sc.)111WS 2019/20SoSe 2024

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information