t.BA.XX.MLDM.20HS (Machine Learning and Data Mining) 
Module: Machine Learning and Data Mining
This information was generated on: 24 April 2024
No.
t.BA.XX.MLDM.20HS
Title
Machine Learning and Data Mining
Organised by
T CAI
Credits
4

Description

Version: 6.0 start 01 February 2024

Short description

 Machine learning and data mining are essential components of successful data products and projects. Students are familiarized with the prerequisites for their use and with various methods for different applications. They study the theoretical fundamentals and the implementation of the methods.

Module coordinator

 Mark Cieliebak (ciel)

Learning objectives (competencies)
Objectives Competences Taxonomy levels
You understand the fundamentals and specialities of Data-Analysis Projects, especially in contrast to Software development projects. F, M K1, K2
You know methods for explorative Data Analysis, especially in the domain of Data Visualization and Feature Engineering. You are also able to successfully apply them in practice. F, M K1, K2, K3
You know methods for knowledge extraction by machine learning applied to structured data. Additionally, you know the difference between machine learning applied to structured in contrast to unstructured data (e.g., images, sound). F, M K1, K2, K3
You know perspectives and opportunities of current research and development in the domain of machine learning. F K1

Module contents

The digitalization of processes and environments is difficult challenge for computer scientists. Software development is hereby not the primary problem, rather the professional processing and analysis of different datatypes and volumes. For this purpose it is essential to have a certain fundamental experience in the area of data analysis and the most important methods in the domain of machine learning.
This module provides you a practical introduction in elemental data mining with methods of machine learning. The focus is on an good overall view and a clean methodology; proofs and details of the methods are not part of this course and are expected to be discussed in later course. The content of the lecture is applied practically by the participants in several projects.

Accompanying Assignments

The lecture is accompanied by practical assignments containing of implementations in python and related tools and libraries with real-world datasets.

Teaching materials
  • Script
  • Slides of the lecture
  • Additional material to the practical assignments

Supplementary literature
 
  • ​Ian H. Witten, Eibe Frank, “Data Mining – Practical Machine Learning Tools and Techniques”, 4th Edition, Elsevier, 2016.

Prerequisites
  • Programming 1&2: Reliable control of a higher procedual or object-oriented programming language
  • Linear Algebra: Vector- and Matrix calculations, inverse Matrix, eigendecomposition
  • Statistic and Stochastic: Fundamentals of probability calculation, distributions, correlations
  • Algorithms and Data Structures: Algorithmic Thinking

Teaching language

 (X) German ( ) English

Part of International Profile

 ( ) Yes (X) No

Module structure

 Typ 3a
  For more details please click on this link: T_CL_Modulauspraegungen_SM2025

Exams

 The regulation on graded class assignments is binding. However, it may be waived if a formal, written request is made by the lecturer in the first week of the semester.
 
Description Type Form Scope Grade Weighting
Graded assignments during teaching semeste practical exercises and/or quizzes (with mark) written

 

   20%
End-of-semester exam Exam written 90 minutes max. 80 points 80%

Remarks

  

Legal basis

 The module description is part of the legal basis in addition to the general academic regulations. It is binding. During the first week of the semester a written and communicated supplement can specify the module description in more detail.

Note

Course: Maschinelles Lernen und Data Mining - Praktikum
No.
t.BA.XX.MLDM.20HS.P
Title
Maschinelles Lernen und Data Mining - Praktikum

Note

  • No module description is available in the system for the cut-off date of 02 August 2099.
Course: Maschinelles Lernen und Data Mining - Vorlesung
No.
t.BA.XX.MLDM.20HS.V
Title
Maschinelles Lernen und Data Mining - Vorlesung

Note

  • No module description is available in the system for the cut-off date of 02 August 2099.