Teacher
GianAntonio Susto
Department of Information Engineering
gianantonio.susto[at]dei.unipd.it
ING-INF/04

Aim

In the past years, automatic decision-making has been enabled by the advancement of Machine Learning (ML) in several important fields like medical diagnoses, insurance, security, and financial trading. The foundational principles underlying some ML approaches are somehow difficult to understand, given the complexity of some black-box models. The lack of interpretability raises several questions: can we trust the outcome of an ML module? Is the outcome of the ML module fair? What really matters in an ML model? Such questions are fundamental for the two main classes of researchers using ML:

- ‘ML Users’, for example in the field of psychology, for which ML models are used as tools and where interpretability can be exploited for validating hypotheses and for designing new experiments;

- ‘ML Developers’, for example in the field of computer science, for which the interpretability of a model can be exploited for understanding complex models. The aim of this course is to motivate the importance of interpretability in the context of Machine Learning and to present some ML algorithms and procedures that have a certain level of interpretability.

Syllabus
- The broad impact of Machine Learning (and data) in science;
- Importance of Interpretability in Machine Learning;
- Taxonomy of Interpretability;
- Model-specific Machine Learning approaches;
- Model-agnostic Methods for Interpretability.

Introductory reading
C. Molnar Interpretable Machine Learning: A Guide for Making Black Box Models Explainable https://christophm.github.io/interpretable-ml-book/

Course requirements
- Basic notions of supervised machine learning (from the BMCS course on Tools and applications of machine learning)

Examination modality
None

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

Schedule
25 March 2024, 10:00 9:00-12:00 (Room 1BC45)
26 March 2024, 10:00 9:00-12:00 (Room 2AB40)
27 March 2024, 10:00-12:00 (Room 2AB40)

Location
Room described ain the schedule above and located at the Dept of Mathematics.

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