700.325 (19W) Practical Introduction to Neural Networks and Deep Learning

Wintersemester 2019/20

Registration deadline has expired.

First course session
07.01.2020 09:30 - 19:30 V.1.04 On Campus
... no further dates known

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

List of events is loading...

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

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

  • 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)
  • 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)
  • 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)
  • 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)
  • 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)
  • 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)

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)