Teacher
Merylin Monaro
Department of General Psychology
merylin.monaro[at]unipd.it
PSIC-03/A

Nicolò Navarin
Department of Mathematics
nicolo.navarin[at]unipd.it
INFO-01/

Aim
The course is divided into two parts. The first part provides a practical understanding of basic machine learning tools and their use. The second part of the course is focused on understanding how machine learning techniques can be applied to answer research questions in psychology (case studies).

Syllabus
• Introduction to machine learning tools and their correct use.
• What machine learning tools can achieve: supervised vs unsupervised techniques.
• Examples of applications and how to assess performances.
• Introduction to cognitive services.
• Case study: “Reading minds” with machine learning and EEG. Case study on brain-computer interfaces.
• Case study: “Reading minds deeper” with fMRI data. Case study on image reconstruction from brain activity.
• Case study: Personality prediction from social data. Case studies on Facebook and Twitter.
• Case study: Predicting sexual orientation from faces. Considerations on reproducibility, bias in data, the burden of proof, and why it is important to explain what machine learning models learn.
• Case study: Emotion recognition from images and multimodal data.
• Case study: Machine Learning for lie detection (e.g., fake identities, fake reviews).
• Case study: Machine Learning for neuromarketing, with guest intervention on recommender systems.
• Guest intervention: “Machine Learning in brain imaging”.

Course requirements
The student is expected to have basic knowledge of probability. Basic programming skills are a plus. Students of the neuroscience curriculum are recommended to have taken "Python for non-computer scientists"

Examination
Modality - The PhD students are asked to carry on a group interdisciplinary project involving the application of machine learning techniques to answer research questions in psychology. These projects are done collaboratively by cross-curricular groups. In the last lecture of the course, the students will present their projects. The projects will be evaluated based on the level of collaborative work required, and the quality of the experimental design and technical skills. Moreover, students are encouraged to interact with the presentations of their colleagues.
Goal - To prepare students to conduct inter-disciplinary research, and practice their ability to apply theoretical knowledge to real-world problems.
Skills acquired with the exam -  Basic understanding of machine learning techniques; ability to recognise and properly formulate a machine learning problem; technical abilities in developing a machine learning pipeline; ability to work collaboratively with colleagues with a different background,
Feedback - After each group’s presentation, general feedback is provided in class. The exam feedback is provided to each student in the Moodle platform or via email.

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

Schedule
20 January 2025, 09:30-12:30 (Room 2AB40)
22 January 2025, 14:00-17:00 (Room 1BC45)
24 January 2025, 14:00-17:00 (Room 1C150)
27 January 2025, 14:00-17:00 (Room 1BC45)
29 January 2025, 14:00-17:00 (Room 2AB40)
31 January 2025, 14:00-17:00 (Room 2AB40)
11 February 2025, 14:00-17:00 (Room 2BC60)

Location
Room specified in the schedule above at the Dept. of Mathematics, via Trieste 63 Padova

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