Machine Learning techniques to predict brain, mind and behaviour

Nicolò Navarin
Dipartimento di Matematica, Università degli Studi di Padova,


The course is addressed to students who would like to understand how machine learning techniques can be applied to answer research questions in psychology. The course will consist of several case studies, covering a broad range of applications from cognitive science (e.g. emotion recognition), cognitive neuroscience (e.g. fMRI analysis), social psychology (e.g. social network analysis), personality psychology (e.g. personality prediction). The final aim of the course is to make students familiar with state-of-the-art machine learning techniques adopted to analyze different kinds of data (temporal series as in EEG, visual data, behavioral data etc.). Students will be encouraged to discuss the limitations of current studies and brainstorm future research developments

- “reading minds” with machine learning and EEG
- “reading minds deeper” with fMRI data. Case study on image reconstruction
- personality prediction from social data: case studies on Facebook and Twitter
- Machine Learning for lie detection (Guest intervention: Dr. Merylin Monaro)
- Neurological disorders classification with 3D Convolutional Neural Networks (Guest intervention: Dr. Cristina Scarpazza, Ivano Lauriola)
- Emotion recognition from images and multimodal data

Introductory reading
Nishimoto, S., Vu, A. T., Naselaris, T., Benjamini, Y., Yu, B., & Gallant, J. L. (2011). Reconstructing visual experiences from brain activity evoked by natural movies. Current Biology, 21(19), 1641–1646.

Course requirements

Examination modality

Course material, enrollment and last minute notifications
Made available by the teacher at this Moodle address

11 March 2019, 14:00-16.30
14 March 2019, 14:00-16.30
18 March 2019, 14:00-16.30
19 March 2019, 14:00-16.30

HIT centre, via Luzzatti 4, meeting room

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