Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Module INF-71-56-M-6

Applications of Machine Learning and Data Science (M, 4.0 LP)

Module Identification

Module Number Module Name CP (Effort)
INF-71-56-M-6 Applications of Machine Learning and Data Science 4.0 CP (120 h)

Basedata

CP, Effort 4.0 CP = 120 h
Position of the semester 1 Sem. in WiSe
Level [6] Master (General)
Language [EN] English
Module Manager
Lecturers
Area of study [INF-KI] Intelligent Systems
Reference course of study [INF-88.79-SG] M.Sc. Computer Science
Livecycle-State [NORM] Active

Notice

old title: Applications of Artificial Intelligence

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V+1U INF-71-56-K-6
Applications of Machine Learning and Data Science
P 42 h 78 h
U-Schein
ja PL1 4.0 WiSe
  • About [INF-71-56-K-6]: Title: "Applications of Machine Learning and Data Science"; Presence-Time: 42 h; Self-Study: 78 h
  • About [INF-71-56-K-6]: The study achievement "[U-Schein] proof of successful participation in the exercise classes (ungraded)" must be obtained.
    • It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: written exam (Klausur) (60-180 Min.)
  • Examination Frequency: each semester
  • Examination number: 67156 ("Applications of Machine Learning and Data Science")

Evaluation of grades

The grade of the module examination is also the module grade.


Contents

  • Approaches for analysing and explaining Time Series
  • Time Series Forecasting and Neural Architecture Search
  • Smart Grid Monitoring and Assessment
  • Attention-Based Natural Language Processing
  • Natural Language Generation
  • Meta-Learning or Learning to Learn
  • Adversarial attacks and Links to Interpretability
  • Video Object Segmentation
  • The intricacies of scaling in neural network training
  • Knowledge Graph Construction
  • Self-organizing Personal Knowledge Assistants

Competencies / intended learning achievements

Upon successful completion of the module, students will be able to
  • explain the special features of industrially and economically relevant fields of application of the AI
  • identify suitable applications of Artificial Intelligence in real contexts,
  • identify concrete project requirements as problems of Artificial Intelligence,
  • plan the application of advanced AI procedures in practice-oriented real-world environments
  • analyze the performance of certain AI procedures
  • reflect applications of Artificial Intelligence in real contexts.

Literature

  • Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.
  • Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
  • Dengel, Andreas, Semantische Technologien – Grundlagen. Konzepte. Technologien. Spektrum Akademischer Verlag, Springer Berlin Heidelberg (Oct. 2011), 427 pages (in German).

Requirements for attendance of the module (informal)

Modules:

Requirements for attendance of the module (formal)

None

References to Module / Module Number [INF-71-56-M-6]

Course of Study Section Choice/Obligation
[INF-88.79-SG] M.Sc. Computer Science [Specialisation] Specialization 1 [WP] Compulsory Elective
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
[EIT-88.D55-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
Module-Pool Name
[EIT-AC-MSC-TW-MPOOL-7] General Elective Modules Master A&C
[GS-CVT-CS-2022-E-MPOOL-6] Catalog Electives Computer Science 2022
[GS-CVT-CS-E-MPOOL-6] Catalog Electives Computer Science
[INF-SIAK-DT-AI-MPOOL-6] SIAK Certificate "Digital Transformation" - Modules INF "Artificial Intelligence"