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Deep-learning for cognitive computing for developers

Individual course

Max amount of FITech students: 20

By any measure, the past few years have been landmark years for the discussion around artificial intelligence (AI) and its potential impact on business and society. Being based on artificial intelligence, cognitive computing systems are “systems that learn at scale, reason with purpose and interact with humans naturally”. Cognitive computing solutions encompass machine learning, reasoning, natural language processing, deep learning, speech and vision, human-computer interaction and more.

This course aims to provide a practical view to the domain of cognitive computing and machine intelligence. Students will learn how to build solutions based on machine intelligence using corresponding open-source software libraries (e.g. TensorFlow). At the same time, students will be capable to design and build their own services and apps using cloud-based cognitive services of big competing players in this field, such as IBM, Google, Microsoft, etc.

Course contents

Introduction to cognitive computing and deep learning and their application for computer vision, NLP and reinforcement learning domains.

Course material

All the study related materials are available on the course webpage.

Teaching schedule

  • Lectures on Thursdays at 12:15–14 (not recorded).
  • Demo sessions on Fridays at 12:15–14 or 14:15–16 are meant for tasks realisation delivery by students and discussions.

Completion methods

Exercises (100 %). Completion of the course tasks brings 8 credits. Extra 2 credits will be given for completion of an optional mini project.

More information in the University of Jyväskylä study guide and on the course page.

You can get a digital badge after completing this course.

Deep Learning, tekoäly, koneoppiminen, AI, syväoppiminen, NLP, kognitiivinen tiedonkäsittely, Artificial Intelligence, Cognitive Computing, TensorFlow, Neural Networks

Responsible teacher

University of Jyväskylä
Oleksiy Khriyenko

Further information about the course and studying

University of Jyväskylä
Annemari Auvinen

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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ICT Studies
5G technology,
AI and machine learning,
Data science,
Course code:
8–10 ECTS
0 €
Teaching period:
Application deadline:
Application period has ended
Host university:
University of Jyväskylä
Study is open for:
Adult learner,
Degree student
Teaching methods:
General prerequisites:
The course is practical and requires at least basic skills in programming (Python is the main programming language). A basic knowledge of SOA and cloud computing, data mining, artificial intelligence, knowledge engineering and natural language processing are recommended.
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