Module 39-M-Inf-ADA Advanced Data Analysis

Faculty

Person responsible for module

Regular cycle (beginning)

Mindestens jedes 2. Wintersemester

Credit points and duration

5 Credit points

For information on the duration of the modul, refer to the courses of study in which the module is used.

Competencies

Non-official translation of the module descriptions. Only the German version is legally binding.

Within this module, students learn how to model complex tasks by means of advanced techniques and methods of data analysis. This includes in particular the question how useful information can be extracted in complex settings without a clear specified objective, and how such basic mathematical models with suitable regularisation or prior can be turned towards efficient algorithms and accompanying theoretical guarantees.

Content of teaching

Within this module, the focus lies on modern techniques for automated data analysis with a particular focus on its efficient representation, formalisation, and algorithmic realisation. Topics are taken from the recent research literature, touching on aspects such as slow feature analysis, sparse coding and compressed sensing, core vector machines, time series metrics, and Gaussian processes.

Recommended previous knowledge

Introduction to computer science (such as algorithms and data structures), mathematics, basic knowledge in machine learning or pattern recognition

Necessary requirements

Explanation regarding the elements of the module

In some degree programmes, the module (partial) examination can also be "ungraded" at the student's discretion. A corresponding specification must be made before the module is taken; a subsequent change (graded - ungraded) is not possible. If the ungraded option is selected, it is not possible to use this module for a degree programme in which this module is taken into account in the overall grade calculation.

Module structure: 0-1 bPr, 0-1 uPr 1

Courses

Modern Data Analysis
Type project o. exercise
Regular cycle Mindestens jedes 2. Wintersemester
Workload5 30 h (30 + 0)
LP 1
Modern Data Analysis
Type lecture
Regular cycle Mindestens jedes 2. Wintersemester
Workload5 60 h (30 + 30)
LP 2

Examinations

portfolio with final examination
Allocated examiner Person responsible for module examines or determines examiner
Weighting without grades
Workload 60h
LP2 2

In some degree programmes of the Faculty of Technology, the module examination can also be "ungraded" at the student's discretion (see explanations of the module elements and the respective subject-specific regulations). If the ungraded option is selected, it is not possible to use this module for a degree programme in which this module is taken into account in the overall grade calculation.
See below for explanations of this examination (graded examination option).

portfolio with final examination
Allocated examiner Person responsible for module examines or determines examiner
Weighting 1
Workload 60h
LP2 2

Portfolio consisting of per default two-weekly exercises or programming tasks and final oral exam (per default 15 minutes). The exercises are based on the content of the lecture and enable students to train and further investigate the topics. It is required that a sufficient percentage of the exercises are successfully completed (per default 50% of the total number of points which can be achieved during a semester). The final oral exam concerns both, the content of the lecture as well as the exercises.

The module is used in these degree programmes:

Degree programme Profile Recom­mended start 3 Duration Manda­tory option 4
Bioinformatics and Genome Research / Master of Science [FsB vom 30.09.2016 mit Änderungen vom 15.09.2017, 02.05.2018, 04.06.2020 und 31.03.2023] 1. o. 3. one semester Compul­sory optional subject
Bioinformatics and Genome Research / Master of Science [FsB vom 17.12.2012 mit Änderungen vom 15.04.2013, 15.10.2014, 02.03.2015, 17.08.2015 und Berichtigungen vom 17.11.2014 und 01.12.2015] 1. o. 2. o. 3. one semester Compul­sory optional subject
Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] Variante 1 3. one semester Compul­sory optional subject
Data Science / Master of Science [FsB vom 06.04.2018 mit Änderungen vom 01.07.2019, 02.03.2020, 21.03.2023 und 10.12.2024] Variante 2 3. one semester Compul­sory optional subject
Intelligent Systems / Master of Science [FsB vom 27.07.2018 mit Änderung vom 04.06.2020] 1. o. 2. o. 3. one semester Compul­sory optional subject
Intelligent Systems / Master of Science [FsB vom 17.12.2012 mit Änderungen vom 15.04.2013, 01.04.2014, 15.10.2014, 02.03.2015 und Berichtigung vom 17.11.2014] 1. o. 2. o. 3. one semester Compul­sory optional subject
Informatics for the Natural Sciences / Master of Science [FsB vom 30.09.2016 mit Berichtigung vom 10.01.2017 und Änderungen vom 15.09.2017, 02.05.2018, 04.06.2020 und 31.03.2023] 1. o. 2. o. 3. one semester Compul­sory optional subject
Informatics for the Natural Sciences / Master of Science [FsB vom 17.12.2012 mit Änderungen vom 15.04.2013, 01.04.2014, 15.10.2014, 02.03.2015, 01.12.2015 und Berichtigungen vom 01.04.2014, 17.11.2014 und 12.07.2017] 1. o. 2. o. 3. one semester Compul­sory optional subject

Automatic check for completeness

The system can perform an automatic check for completeness for this module.


Legend

1
The module structure displays the required number of study requirements and examinations.
2
LP is the short form for credit points.
3
The figures in this column are the specialist semesters in which it is recommended to start the module. Depending on the individual study schedule, entirely different courses of study are possible and advisable.
4
Explanations on mandatory option: "Obligation" means: This module is mandatory for the course of the studies; "Optional obligation" means: This module belongs to a number of modules available for selection under certain circumstances. This is more precisely regulated by the "Subject-related regulations" (see navigation).
5
Workload (contact time + self-study)
SoSe
Summer semester
WiSe
Winter semester
SL
Study requirement
Pr
Examination
bPr
Number of examinations with grades
uPr
Number of examinations without grades
This academic achievement can be reported and recognised.