700.395 (22W) Data Mining, Synthetic Data and Knowledge Discovery

Wintersemester 2022/23

Registration deadline has expired.

First course session
10.12.2022 09:00 - 17:00 Online Off Campus
... no further dates known

Overview

Due to the COVID-19 pandemic, it may be necessary to make changes to courses and examinations at short notice (e.g. cancellation of attendance-based courses and switching to online examinations).

For further information regarding teaching on campus, please visit: https://www.aau.at/en/corona.
Lecturer
Course title german Data Mining, Synthetic Data and Knowledge Discovery
Type Lecture - Course (continuous assessment course )
Course model Online course
Hours per Week 2.0
ECTS credits 4.0
Registrations 27 (30 max.)
Organisational unit
Language of instruction Englisch
Course begins on 10.12.2022
eLearning Go to Moodle course
Seniorstudium Liberale Yes

Time and place

Please note that the currently displayed dates may be subject to change due to COVID-19 measures.
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Course Information

Intended learning outcomes

Data Mining and Neurocomputing have different applications in text categorization, e.g., spam filtering, fraud detection, optical character recognition, machine vision, e.g., face detection, licenses plate recognition, advanceddriver assistance systems, natural-language processing, e.g., spoken language understanding, market segmentation, e.g., predict if a customer will get a credit, and  bioinformatics, e.g.,  classify proteins or lipidomes according to their function.

The lecture will (a) explain the basic approaches of Data Mining and Neurocomputing models, (b) guide to transfer the acquired knowledge to solve supervised and unsupervised problems for industry and research, and (c) show some use-cases and interesting applications from the state-of-the-art.

Teaching methodology including the use of eLearning tools

Slides + Exercises (Live Demonstration), Online Quiz

Course content


  • Data preprocessing
  • Dimensionality Reduction (Singular Value Decomposition (SVD), Principal Component Analysis (PCA) )
  • Unsupervised Learning and Clustering (K-means, Expectation-Maximization)
  • Supervised Learning (Support Vector Machine (SVMs), Bayes Classifiers, Decision Trees)
  • Regularization Techniques
  • Kernel Models
  • Recommender Systems (Collaborative Filtering and Association Rule Mining)
  • Introduction to  Neurocomputing (Activation Functions, Backpropagation, Perceptron and Multi layer perceptron (MLP), A brief Introduction on Recurrent Neural Networks  and Convolutional  Neural Networks)
  • Evaluation Metrics

Literature

M. Bishop, Pattern Recognition and Machine Learning, Springer

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.

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Master's degree programme 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, Synthetic Data and Knowledge Discovery (2.0h VC / 4.0 ECTS)
  • Master's degree programme 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, Synthetic Data and Knowledge Discovery (2.0h VC / 4.0 ECTS)
  • Master's degree programme 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, Synthetic Data and Knowledge Discovery (2.0h VC / 4.0 ECTS)
  • Master's degree programme 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, Synthetic Data and Knowledge Discovery (2.0h VC / 4.0 ECTS)
  • Master's degree programme 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, Synthetic Data and Knowledge Discovery (2.0h VC / 4.0 ECTS)
  • Master's degree programme 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, Synthetic Data and Knowledge Discovery (2.0h VC / 4.0 ECTS)
  • Master's degree programme Information and Communications Engineering (ICE) (SKZ: 488, Version: 22W.1)
    • Subject: Autonomous Systems and Robotics: Advanced (Compulsory elective)
      • 2.2 Data Mining and Neurocomputing ( 0.0h VC / 4.0 ECTS)
        • 700.395 Data Mining, Synthetic Data and Knowledge Discovery (2.0h VC / 4.0 ECTS)
  • Master's degree programme Mathematics (SKZ: 401, Version: 18W.1)
    • Subject: Information and Communications Engineering (Compulsory elective)
      • 9.5 Data Mining and Neurocomputing ( 2.0h VC / 4.0 ECTS)
        • 700.395 Data Mining, Synthetic Data and Knowledge Discovery (2.0h VC / 4.0 ECTS)

Equivalent courses for counting the examination attempts

Wintersemester 2024/25
  • 700.395 VC Data Mining, Synthetic Data, and Knowledge Discovery (2.0h / 4.0ECTS)
Wintersemester 2023/24
  • 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 2017/18
  • 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)