700.395 (17W) Data Mining and Neurocomputing

Wintersemester 2017/18

Registration deadline has expired.

First course session
20.11.2017 13:30 - 20:00 HS 11 On Campus
... no further dates known

Overview

Lecturer
Course title german Data Mining and Neurocomputing
Type Lecture - Course (continuous assessment course )
Hours per Week 2.0
ECTS credits 4.0
Registrations 23 (25 max.)
Organisational unit
Language of instruction English
possible language(s) of the assessment German , English
Course begins on 20.11.2017
eLearning Go to Moodle course
Seniorstudium Liberale Yes

Time and place

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Course Information

Intended learning outcomes

The students

  • are able use the basic methods for datamining and neurocomputing (Deep Learning),
  • have a good command of  datamining approaches for different applications
  • are able to transfer the acquired knowledge to solve complex machine learning  applications for industry and research,
  • are able to describe the state-of-the-art of the presented topics

Teaching methodology including the use of eLearning tools

The course has two major parts. The first part consists of the theoretical and methodic fundamentals that will be introduced during the lecture. The second part consists of intensive lab work where students can implement, test, and apply the presented methods. The major lab language will be python using TensorFlow and Scikit-Learn libraries. 

Course content

The course focuses  on data selection, cleaning and preparation. Then, it covers  the topics of dimension reduction, for example using principle components analysis and Auto-encoders. Additionally, different types of linear and non-linear models will be discussed in details with focus on neural models. Finally, different evaluation metrics and performance analysis s will be presented.

  • Mathematical basics                                                            
  • Data selection and preparation                                                            
  • Bayesian models                                                            
  • Linear models                                                            
  • Non linear models                                                            
  • Artificial Neural Networks
  • Feedforward Neural Networks
  • Backpropagation
  • Recurrent Neural Networks                                                            
  • Deep Learning                                                            
  • Evaluation Metrics      

Prior knowledge expected

Basic knowledge of any programming language

Literature

Data Mining: Practical Machine Learning Tools and Techniques# Publisher: Morgan Kaufmann; 2 edition (June 22, 2005)# Ian H. Witten, Eibe Frank# Language: English# ISBN-10: 0120884070# ISBN-13: 978-0120884070

Principles of Neuro Computing for Science & Engineering# Fredric M. Ham# Ivica Kostanic# ISBN: 0-07-025966-6# Publisher: McGraw-HillGeorge Papadourakis, “Introduction to Neural Networks”, Lecture Notes

Examination information

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Examination methodology

Written Exam + Project 

Examination topic(s)

The student should pass a written exam successfully which reflects the understanding of the presented concepts and approaches. Additionally, the students should design and implement a fully working system in the field of machine learning.

Assessment criteria / Standards of assessment for examinations

The final evaluation is divided as follows:

70% Exam

30% Project


Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Information and Communications Engineering: Supplements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Technical Complements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Information and Communications Engineering: Supplements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 4) ( 0.0h VK, VO, KU / 14.0 ECTS)
        • 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Technical Complements (NC, ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (Anhang 5) ( 0.0h VK, VO, KU / 12.0 ECTS)
        • 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Autonomous Systems and Robotics: Advanced (ASR) (Compulsory elective)
      • Wahl aus dem LV-Katalog (siehe Anhang 3) ( 0.0h VK, VO / 30.0 ECTS)
        • 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
  • Masterstudium Information and Communications Engineering (ICE) (SKZ: 488, Version: 15W.1)
    • Subject: Autonomous Systems and Robotics (WI) (Compulsory elective)
      • Wahl aus dem LV-Katalog (siehe Anhang 3) ( 0.0h VK, VO / 30.0 ECTS)
        • 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)
  • Master's degree programme Information Technology (SKZ: 489, Version: 06W.3)
    • Subject: Research Track (Methodological focus) (Compulsory subject)
      • 4.2'-4.3' Theoretical methodological courses I/II ( 0.0h VO/VK/VS/KU/PS / 6.0 ECTS)
        • 700.395 Data Mining and Neurocomputing (2.0h VC / 4.0 ECTS)

Equivalent courses for counting the examination attempts

Wintersemester 2023/24
  • 700.395 VC Data Mining, Synthetic Data, and Knowledge Discovery (2.0h / 4.0ECTS)
Wintersemester 2022/23
  • 700.395 VC Data Mining, Synthetic Data and Knowledge Discovery (2.0h / 4.0ECTS)
Wintersemester 2021/22
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2020/21
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Sommersemester 2020
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Sommersemester 2019
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2016/17
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2015/16
  • 700.395 VC Data Mining and Neurocomputing (2.0h / 4.0ECTS)
Wintersemester 2013/14
  • 700.395 VK Data Mining in Intelligent Transportation and Logistics (2.0h / 4.0ECTS)
Wintersemester 2012/13
  • 700.395 VK Data Mining in Intelligent Transportation and Logistics (2.0h / 4.0ECTS)