-applications, best practices and perspectives

The University of Oslo, dScienceSINTEF, Norwegian Computing CenterStartupLab and Oslo Science Academy invite data scientists, engineers, and developers to a 9-week hands-on course in Deep Neutral Networks (DNN). 



17 500,- (15 000,- for partners)


Start date:

April 6, 2021



A 9 week course series, 2 hours per week,
(one hour lecture followed by one hour of lab exercises)


Between 14:00 – 16:00 (N.B. time might vary)



Physical attendance in Oslo Science Park or online streaming
Due to Covid-19 the lectures will be held as webinars, if needed  







The University of Oslo (UiO) is seeing high demand for skilled data scientists from all industry sectors, and a general interest in Deep Learning and Artificial Intelligence. To provide more skilled workers for the industry, UiO is joining forces with nearby Research Institutes to offer a compact program aimed at corporate employees.  


The course is tailored for experienced data scientists, engineers and developers who want to extend their knowledge into Machine Learning. The 9-week course will get the participants started with Deep Learning and help them understand its opportunities, challenges, and limitations. The course approach is practical, giving hands-on experience and empowering the participants to better understand methodologies and technology components needed for successful use of Deep Neural Networks (DNNs)  


Course format
The course will start with an introductory lecture on the evolution of Deep Neural Networks, followed by eight lectures combining presentations and lab work. Every lecture is followed by a 1-hour lab exercise exploring data sets related to the lecture. For each lecture there will be a pre-lab to set up the dataset used in class. The four first lectures aim to provide sufficient general and technical knowledge about Deep Neural Networks to support the subsequent and application-oriented lectures. The course will be experimental and reflective and seeks to combine insights and best practices across problem areas. At the end of the 8-week program, the closing lecture will focus on the impact of AI and Deep Learning on industry and society.



Schedule: (changes may occur)

  • April 6 - Physical attendance
    "The Evolution and History of Deep Neural Networks – what has happened since the 80es?", by Professor Ole-Christian Lingjærde, Department of Informatics, Research Group for Biomedical Informatics, UiO.

  • April 13: program is coming soon

  • April 20 - Physical attendance
    "First principles of Neural Networks and Deep Learning", by . Dr. Arnt Børre Salberg, Norwegian Computing Centre (NR).

  • April 27 - online streaming
    "Image Recognition with Deep Neural Networks", by Anne Solberg, Professor, Department of informatics, Research group for digital signal processing and image analysis, UiO.

  • May 4 - Physical attendance
    "Neural networks to improve medical diagnosis and prognosis: When will it work?", by Professor. Knut Liestøl, Department of Informatics, Research Group for Biomedical Informatics, UiO.

  • May 11 - Physical attendance
    "The first successful Deep Learning Application – Speech Recognition", by Dr. Hans Jørgen Bang, Chief Scientist Officer, sensiBel

  • May 19 - Physical attendance
    "How to train Deep Neural Networks", by Dr. Arnt Børre Salberg, Norwegian Computing Centre (NR).

  • May 25: Program is coming soon 

  • June 1: Program is coming soon 

Lab exercises 
There will be six one-hour lab sessions, following lectures 2 through 7. Each participant must bring a laptop and should be familiar with the Python programming language. The exercises are designed to show in more detail how the concepts and methods discussed in the preceding lecture work in practice and how they can be applied to relevant data sets. The lab exercises will give participants hands-on experience with tools and techniques that can be used on a wide set of real-world problems. The goal is to give participants both an overview of relevant techniques as well as a clear idea of how to solve similar problems related to their own work. The lecturer and lab assistants will be present for guidance and to answer questions.