Learning Objectives:
- Activate and consolidate prior knowledge of statistics, programming and data analysis
- Acquire fundamental knowledge of modelling, optimisation and classification
- Develop analytical skills by exploring notions of distance, reduction, error and data fit
- Know the basic principles of machine learning through various types of algorithms, supervised or not, mastering their parameterization, optimization and applicability conditions
- Apply the principles of machine learning acquired in real neuroscientific data
- Explore more advanced machine learning concepts, including Bayesian and neural network analysis
The teaching methods are designed to promote an integration between theoretical knowledge and practical application. Lectures will provide the conceptual foundation, while hands-on labs and projects will allow students to directly apply knowledge in real-world neuroscientific data analysis situations.
