700.395 (17W) Data Mining and Neurocomputing
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
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
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 schemePosition 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)
-
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)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- 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)
-
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)
- 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)
-
Wahl aus dem LV-Katalog (Anhang 5) (
0.0h VK, VO, KU / 12.0 ECTS)
-
Subject: Technical Complements (NC, ASR)
(Compulsory elective)
- 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)
-
Wahl aus dem LV-Katalog (siehe Anhang 3) (
0.0h VK, VO / 30.0 ECTS)
-
Subject: Autonomous Systems and Robotics: Advanced (ASR)
(Compulsory elective)
- 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)
-
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 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)
-
4.2'-4.3' Theoretical methodological courses I/II (
0.0h VO/VK/VS/KU/PS / 6.0 ECTS)
-
Subject: Research Track (Methodological focus)
(Compulsory subject)
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)