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Assistant Professorship for Intelligent Information Systems
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  1. Friedrich-Alexander-Universität
  2. School of Business, Economics and Society

Assistant Professorship for Intelligent Information Systems

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Development of Deep Vision Systems

In page navigation: Teaching
  • Bachelor
    • Business Analytics: Technologien, Methoden und Konzepte
  • Master
    • Development of Deep Vision Systems
    • Natural Language Processing for Business Analytics
    • Business Analytics: Case Studies (Winter Term)
    • Business Analytics: Case Studies (Summer Term)
    • Business Analytics: Research Seminar (Winter Term)
    • Business Analytics: Research Seminar (Summer Term)
  • Theses

Development of Deep Vision Systems

Development of Deep Vision Systems

Computer vision systems try to mimic human capabilities of visual perception to support time-consuming and labor-intensive tasks like the recognition, localization, and tracking of critical objects. Nowadays, such systems increasingly rely on methods and tools from the field of machine learning to automatically extract useful information from images that can be utilized for decision support and business automation purposes.

This course provides the necessary fundamentals for the development of modern vision systems based on machine learning. The particular focus is on deep neural networks and their capabilities of automated feature learning. More specifically, we consider different types of network architectures, look at the steps of image labeling and data preparation, discuss crucial hyperparameters and evaluation criteria, and review other related aspects, such as 3D vision, hybrid intelligence, and explainable artificial intelligence.

The course has a strong practical focus. At the beginning of the semester, all fundamentals are provided in lecture sessions and hands-on exercises using the programming language Python. Afterwards, students are encouraged to work (in groups) on real projects to apply the methods and concepts learned during the teaching sessions. The results are presented and discussed at the end of the semester.

Please note that the number of participants for this course is limited. Below you can find further information on the registration process. After a successful course registration, more details on course updates, course materials, technical setup, and other information will be announced in StudOn.

Learning objectives and skills

The students
  • understand the challenges for developing vision-based systems,
  • understand the basic principles of machine learning and deep neural networks in the realm of image processing,
  • explain the general pipeline of computer vision systems based on deep neural networks,
  • know about state-of-the-art techniques at the intersection of computer vision and machine learning,
  • apply technologies for automated image processing in a practical setting,
  • compare and evaluate different system configurations,
  • work in groups and present their results together,
  • develop skills in collaborative interaction with peers.

Course information

Courses Lecture: Development of Deep Vision Systems (2,5 SWS)

Exercise: Development of Deep Vision Systems (2,5 SWS)

Credits 5 ECTS
Lecturers Prof. Dr. Patrick Zschech (Lecture)

Prof. Dr. Mathias Kraus (Exercise)

Time Block course in the first half of the semester.

Lecture: Monday 9:15 – 11:15 (6 sessions à 120 min)

Exercise: Monday 15:00 – 18:00 (1 intro session à 180 min) and Thursday 15:00 – 18:00 (4 sessions à 180 min)

For further information see schedule.

Location This semester’s course (Winter 2022/2023) will be held exclusively online via Zoom. Please register for the course via StudOn.
Recommended prerequisites Basic knowledge in data analysis techniques, predictive modelling principles, statistics, and machine learning as taught, for example, in the Bachelor course “Business Analytics: Technologien, Methoden und Konzepte”.

Basic programming skills, preferably in Python.

Integration in curriculum Second or third semester
Module compatibility Master International Information Systems (from 2018/19): Module in the section Information Systems – Data & Knowledge (Elective)

Master International Information Systems (from 2016/17): Module in the section Information Systems – Extension Courses (Elective)

Master in Marketing (start before WS17/18): Wahlpflichtbereich Modulgruppe “Methoden”

Master in Marketing (start since WS17/18): Wahlpflichtbereich Modulgruppe “Data Science”

Method of examination Project report and presentations, partly in groups
Grading procedure Project report (80%) and presentation (20%)
Module frequency Each semester (without guarantee)
Workload Contact hours: 75 h

Independent study: 75 h

Module duration 1 Semester
Teaching and examination language English
(Recommended) reading All relevant material will be provided during the course.

Registration

The number of participants is limited and requires a registration via StudOn. The registration period is between September 19 – October 17, 2022. If you register before October 10 there is a higher chance of participation with early bird confirmation. In case of excess demand, participants will be selected by drawing lots. Firm confirmation of registration at the latest until October 24, 2022.

Schedule (lecture, exercise, project seminar)

Please note that the schedule is not yet finalized and we reserve the option for changes.

CW Organization / Lecture Dates / Times Exercise Dates / Times
Course Registration September 19 – October 17, 2022
42
Opening Session
Mon, Oct 17, 9:15-11:15
43
1) Intro Computer Vision
Mon, Oct 24, 9:15-11:15
44
2) Artificial Neural Networks
Mon, Oct 31, 9:15-11:15
Intro Anaconda / Colab Python
Mon, Oct 31, 15:00-18:00
45
3) Convolutional Neural Networks
Mon, Nov 7, 9:15-11:15
Numerical Computations
Thurs, Nov 10, 15:00-18:00
46
Guest Lecture + Project Kick-off
Mon, Nov 14, 9:15-11:15
Intro Deep Learning with Pytorch
Thurs, Nov 17, 15:00-18:00
47
4) Data Preparation, Labeling,
Augmentation + 5) Transfer Learning
Mon, Nov 21, 9:15-11:15
Neural Networks for Classification
Thurs, Nov 24, 15:00-18:00
48
6) Evaluation + 7) Additional Aspects
Mon, Nov 28, 9:15-11:15
Neural Networks for Object Detection
Thurs, Dec 1, 15:00-18:00
49
Consultation
Mon, Dec 5, 9:15-11:15
50
Consultation
Mon, Dec 12, 9:15-11:15
51
Consultation
Mon, Dec 19, 9:15-11:15
2
Consultation
Mon, Jan 9, 9:15-11:15
3
Presentation Preliminary Results
Mon, Jan 16, 9:00-14:30
4
Consultation
Mon, Jan 23, 9:15-11:15
5
Consultation
Mon, Jan 30, 9:15-11:15
6
Consultation / Guest Lecture (Adidas)
Mon, Feb 6, 9:15-11:15
10
Submission Project Seminar
Fr, March 10, 23:59
11
Final Presentation
Mon, March 13, 9:00-14:30

 

FAU Erlangen-Nürnberg
Assistant Professorship for Intelligent Information Systems

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90403 Nürnberg
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