700.325 (19W) Practical Introduction to Neural Networks and Deep Learning
Overview
- Lecturer
- Course title german Practical Introduction to Neural Networks and Deep Learning
- Type Lecture - Course (continuous assessment course )
- Hours per Week 2.0
- ECTS credits 4.0
- Registrations 24 (25 max.)
- Organisational unit
- Language of instruction English
- possible language(s) of the assessment German
- Course begins on 07.01.2020
- eLearning Go to Moodle course
- University entrance qualification examination Yes
Time and place
Course Information
Intended learning outcomes
Neural networks and deep learning have different applications in text categorization, e.g., spam filtering, fraud detection, optical character recognition, machine vision, e.g., face detection, licenses plate recognition, advanced driver 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 cover the practical topics regarding (a) Neural networks and deep learning models, (b) guide to transfer the acquired knowledge to solve classification 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
Theory + practical examples (Python)
Course content
- Data preprocessing
- Unsupervised Learning and Clustering
- Deep Learning (multilayer perceptron, convolutional models, recurrent models)
- Deep learning libraries (torch, theano, keras, tensorflow...etc.)
- Time series forecast
- Evaluation Metrics
Examination information
Grading scheme
Grade / Grade grading schemePosition in the curriculum
- Bachelorstudium Informationstechnik
(SKZ: 289, Version: 17W.1)
-
Subject: Informationstechnische Vertiefung
(Compulsory elective)
-
10a.3 Wahl von Lehrveranstaltungen (
0.0h VO/VC/KS/UE / 6.0 ECTS)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (2.0h VC / 4.0 ECTS)
-
10a.3 Wahl von Lehrveranstaltungen (
0.0h VO/VC/KS/UE / 6.0 ECTS)
-
Subject: Informationstechnische Vertiefung
(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)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (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)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (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)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (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)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (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)
- 700.325 Practical Introduction to Neural Networks and Deep Learning (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)
Equivalent courses for counting the examination attempts
-
Wintersemester 2023/24
- 700.325 KS Lab: Neural Networks and Deep Learning (2.0h / 4.0ECTS)
-
Wintersemester 2022/23
- 700.325 KS Practical Introduction to Neural Networks and Deep Learning (2.0h / 3.0ECTS)
-
Wintersemester 2021/22
- 700.325 VC Practical Introduction to Neural Networks and Deep Learning (2.0h / 4.0ECTS)
-
Wintersemester 2020/21
- 700.325 VC Practical Introduction to Neural Networks and Deep Learning (2.0h / 4.0ECTS)