Project (II) Frameworks and Concept Study (ILV)

Course numberM2.08760.11.091
Course codePROJ2
Semester of degree program Semester 2
Mode of delivery Presence- and Telecourse
ECTS credits5,0
Language of instruction English

Students know different AI frameworks used in industry and research.
They are familiar with cloud services and know how to operate them efficiently.
They know about the advantages and disadvantages of the individual frameworks and their differences and interrelationships.
They are able to use these platforms in a data science project and modell, evaluate and optimize their parameters.
Students are able to define requirements and develop a concept for a selected project.

The module covers the following topics/contents:

  • Application and configuration of popular AI frameworks, such as TensorFlow, Keras, PyTorch, Caffe or Julia etc.
  • Application and configuration of popular cloud services, such as Google Colab, Amazon Cloud Services or IMB Watson etc.
(Services and platforms are exemplary and will be adapted to the respective development in the communities and the market)
Selected project in a selected domain
  • Project setup, planning & management
  • Define requirements
Decide for the best practicable solution to be implemented in the subsequent module Project (III).

Lecture script as provided in the course (required)
Francois Chollet, Deep Learning with Python, Manning, 2nd ed. 2020
Eli Stevens, Luca Antiga, Deep Learning with PyTorch, Manning 2020
Abhishek Kumar Annamraju, Akash Deep Singh, Introduction to Deep Learning with Caffe2, Packt, 2020
Christopher Bourez, Deep Learning with Theano, Packt, 2017

Integrated course - teaching & discussion, demonstration, practical examples.
Apply theoretical and practical concepts from completed modules to develop a concept study with regular supervision by project tutors.

Immanent examination character: assignment reports and presentation of the final concept study (to be implemented in Project (II)).