700.395 (22W) Data Mining, Synthetic Data and Knowledge Discovery
Overview
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
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
Grading scheme
Grade / Grade grading schemePosition 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)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 4) (
0.0h VK, VO, KU / 14.0 ECTS)
-
Subject: Information and Communications Engineering: Supplements (NC, ASR)
(Compulsory elective)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- 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)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Subject: Autonomous Systems and Robotics: Advanced (ASR)
(Compulsory elective)
- 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)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Subject: Autonomous Systems and Robotics (WI)
(Compulsory elective)
- 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)
-
2.2 Data Mining and Neurocomputing (
0.0h VC / 4.0 ECTS)
-
Subject: Autonomous Systems and Robotics: Advanced
(Compulsory elective)
- 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)
-
9.5 Data Mining and Neurocomputing (
2.0h VC / 4.0 ECTS)
-
Subject: Information and Communications Engineering
(Compulsory elective)
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)