Applications, best practices and perspectives

The University of Oslo, SINTEF, Norwegian Computing Centre and StartupLab invite data scientists, engineers and developers to the 3rd edition of our 9-week hands-on course starting October 2019.


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 students to better understand methodologies and technology components needed for successful use of Deep Neural Networks (DNNs).


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


The classes are held weekly on Thursdays (with an exeption for the introductory session on Tuesday 16.10) at Oslo Science Park, Gaustadaléen 21. They start at 2pm and last until 4pm, consisting of a one hour lecture followed by one hour of lab exercises.

"The Evolution and History of Deep Neural Networks – what has happened since the 80es?"

Professor Ole-Christian Lingjærde, Department of Informatics, Research Group for Biomedical Informatics.


Oct 10th

Oct 17th


"Preliminaries for Deep Neural Networks: Recapture of Linear Algebra, Gradient Descents and Generalized Linear Models"

Professor Geir O. Storvik, Department of Statistics, UiO

"The First principles of Neural Networks and Deep Learning"

Anne Solberg, Professor, Department of informatics, Research group for digital signal processing and image analysis, UiO.


Oct 24th

Oct 31st


"Image Recognition with Deep Neural Networks"

Anne Solberg, Professor, Department of informatics, Research group for digital signal processing and image analysis, UiO.

"Neural networks to improve medical diagnosis and prognosis: When will it work?"

Professor. Knut Liestøl, Department of Informatics, Research Group for Biomedical Informatics, UiO


Nov 7th

Nov 14th


"Deep Learning and 3D Industry Automation – Simulation and Implementation of robot binpicking",

Dr. Helene Schulerud, SINTEF Digital

"How to train Deep Neural Networks"

Dr. Arnt Børre Salberg, Norwegian Computing Centre (NR).


Nov 21st

Nov 28th


"The first successful Deep Learning Application – Speech Recognition"

Dr. Hans Jørgen Bang, Chief Scientist, AoD Labs

"The impact of AI and Deep Learning on society and industry"

Professor Nils Damm Christophersen, Centre for Entrepreneurship, Department of Informatics, University of Oslo.


Des 5th


Participants are expected to have a master’s equivalent in some quantitative discipline; statistics, math, engineering, or in a discipline extensively using tools from quantitative areas. Good programming skills are required, and Python is used throughout the course.

Laptops are required, and tablets will not be sufficient for the computing activities performed in this course.

Price: NOK 17.500 per participant.

The number of participants is limited to 30.


Download a PDF version of the full Course Program with more information about the lecturers.

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