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Networked AI systems

Individual course

Max amount of FITech students: 15

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

Discover the crucial link between Networking and AI. Learn how to leverage AI for networking and how networking is key to enabling multiple AI systems.

Course contents

  • Introduction to Networked AI systems
  • Neural networks and deep learning for networks
  • Distributed AI in networks
  • Network design and optimisation for AI
  • AI-based network security
  • Network and services orchestration for AI and using AI
  • Future trends and directions for networked AI systems

Learning outcomes

After having passed the course you will be able to

  • explain the concept of Networked AI systems, including the key components and the benefits and challenges of using AI in networks and networks in AI
  • understand how to apply different machine learning algorithms to network-related problems, including network optimisation, network security, and network management
  • evaluate the performance of AI-based networked solutions, including trade-offs between accuracy, efficiency, and other network-related factors such as latency
  • understand network architecture design requirements for AI-based systems
  • understand the challenges and opportunities of distributed AI in networks (e.g., Edge AI, federated learning, etc.)
  • evaluate the security and privacy aspects of AI in networked systems, including threat models, and privacy-preserving techniques
  • understand the challenges and opportunities of AI in networked systems and their impact on future network design and optimisation
  • design, develop, and deploy a networked AI system as part of a collaborative course project, demonstrating the ability to integrate and coordinate multiple AI components

Course material

All study materials for the course will be available on the Moodle platform. The literature for the course is derived from multiple sources such as research articles and other online material (e.g., tutorials, white papers, video sources).

Teaching schedule

  • Lectures on Mondays at 14–16 and Tuesdays at 10–12 on campus
  • Exercises on Wednesdays at 10–12

Completion methods

  • Lectures, written exercises, programming exercises and possibly other forms of teaching.
  • Activity during the course, including possibly mandatory attendance, will be required to pass the course.

More information in the University of Helsinki study guide.

You can get a digital badge after completing this course.

5G-teknologia, tietoturva, etiikka

Responsible teacher

University of Helsinki
Roberto Morabito, Senior researcher

Further information about the course and studying

University of Helsinki
Reijo Siven

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
Application period has ended
Application period has ended
5G technology,
AI and machine learning,
Data science,
Internet technology
Course code:
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0 €
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Teaching period:
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Host university:
University of Helsinki
Who can apply:
Adult learner,
Degree student
Teaching method:
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General prerequisites:
Ability to independently develop functionally complete and tested programs using some modern language (e.g., Python, Java, C++). Basic understanding of following concepts: networked systems and services, artificial intelligence and machine learning (such as supervised and unsupervised learning, neural networks). Recommended: Experience in project work as a team member developing a software solution (an application or a service), knowledge of programming languages and frameworks commonly used in AI, such as Python and TensorFlow and knowledge of distributed computing concepts, such as parallel computing and distributed systems.
Course suitable for:
People who need to understand localisation and positioning technologies.
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