Courses at partner institutions

Below you will find an overview of the courses at NORAs partner institutions within artificial intelligence, machine learning and robotics. 

1. First VIVA European Summer School on Artificial Intelligence and Software           Verification and Validation

Facing the challenge of building trustworthy AI-based software and systems, Europe needs to educate a new generation of bright researchers on the usage of AI in Software Verification and Validation (V&V) and the V&V aspects of AI systems. To reach this goal, we propose to welcome about 30 PhD students in VIVA, the 1st summer school dedicated to AI and Software V&V. High-level courses will be given by the most recognized European researchers in the field of Software V&V and trustworthy Artificial Intelligence and will include an appropriate combination of theoretical and practical matters. The courses will cover a broad spectrum of research topics including Machine Learning and Data Mining in Software Testing, V&V of Neural Networks, Constraint Programming in Verification Testing of Autonomous Systems, etc. The PhD School is co-organized by LIRMM, Simula Research Laboratory and Inria with the financial support of the French Institute in Norway, Simula, Inria and CNRS through the GDR-IA, GDR-MADICS and the GDR-GPL.

  •  Location: Belambra Club, La Grande Motte, Montpellier, France.
  • Dates: 15 - 19 June 2020
  • Organizers: Arnaud Gotlieb (Simula Research Laboratory, Oslo), Nadjib Lazaar (LIRMM, University of Montpellier) and Florent Masseglia (LIRMM, Inria).

For more information click here.

2. Machine Learning Fundamentals

On February 19-20, Simula Consulting together with Machine Learning Genova Center organises a course on Machine Learning Fundamentals. We here provide a modified version of the long-standing successful courses taught at the University of Genova and MIT, and focus on the fundamental methods of modern ML, providing participants with the knowledge needed to get started with ML. The sessions on theoretical and algorithmic aspects will be complemented by hands-on training using Jupyter notebooks. The participants will also have an opportunity for a face-to-face session with the instructors to discuss individually relevant ML challenges.

​The course is organised jointly by Simula Consulting and Machine Learning Genova Center (MaLGa)

For more information click here.

3. Neuro-insights: Data Science Approaches in Neuroscience I

This course provides a comprehensive introduction to data science and big data applied to neuroscience research. Its content is designed to train the participants in state-of-the-art techniques in data analysis and machine learning. This will enable the students to interact independently with the data and draw insights from them. The modules are organized so the participants have the opportunity to learn how to handle the most common data types (e.g., EEG, calcium imaging). Special attention is given to field-tested data management protocols, as they are critical for a fast transition from data acquisition to knowledge generation.

This is a hands-on course where the students will learn from implementing the analysis themselves with close supervision. The course will focus on case studies using data from real experiments; advanced students may choose to use their own data. The students will develop understanding through the constant presentation of their work and dialectical reflection over their choices, results, and interpretations.

​The course is organised by Oslo Metropolitan University. Application deadline February 1st 2020.

For more information click here!

4. Neuro-insights: Data Science Approaches in Neuroscience II

This course provides a comprehensive introduction to data science and big data applied to neuroscience research. Its content is designed to train the participants in state-of-the-art techniques in data analysis and machine learning. This will enable the students to interact independently with the data and draw insights from them. The modules are organized so the participants have the opportunity to learn how to handle the most common data types (e.g., EEG, calcium imaging). Special attention is given to field-tested data management protocols, as they are critical for a fast transition from data acquisition to knowledge generation.

This is a hands-on course where the students will learn from implementing the analysis themselves with close supervision. The course will focus on case studies using data from real experiments; advanced students may choose to use their own data. The students will develop understanding through the constant presentation of their work and dialectical reflection over their choices, results, and interpretations.

​The course is organised by Oslo Metropolitan University. Application deadline February 1st 2020.

For more information click here!

Publisert 14. jan. 2020 13:21 - Sist endret 11. mars 2020 11:19