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| Academic Year |
2024/2025 |
| Name |
Dott. - MI (1380) Ingegneria dell'Informazione / Information Technology |
| Programme Year |
1 |
| ID Code |
062777 |
| Course Title |
INTERPRETABILITY AND EXPLAINABILITY IN MACHINE LEARNING |
| Course Type |
MONO-DISCIPLINARY COURSE |
| Credits (CFU / ECTS) |
5.0 |
| Course Description |
"Machine Learning is becoming ubiquitous and data-driven models are increasingly used to make high-stakes decisions in sensitive domains such as healthcare, safety systems, education, and criminal justice. Accordingly, it is important to ensure that decision-makers properly understand how these models work such that they can trust their outputs.
The course will cover the following topics.
Fundamentals: an overview of the field and seminal papers, the definition of interpretability and explainability, scope of interpretability, properties of explanations, evaluating interpretability.
Interpretable Models: interpretability properties of linear regression, logistic regression, decision trees, rule-based techniques, generalized additive models, and instance-based approaches.
Model-Agnostic Methods: partial dependence plot, individual conditional expectation, accumulated local effects plot, feature interaction, feature importance, Shapley additive explanations, local and global surrogates.
Example-Based Explanations: counterfactual explanations, adversarial examples, prototypes, and influential instances
Advanced Topics: Interpretability of Neural Networks and Deep Learning, interpretability and causality, fairness, debugging, human-in-the-loop approaches." |
| Scientific-Disciplinary Sector (SSD)
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SSD Code
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SSD Description
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CFU
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ING-INF/05
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INFORMATION PROCESSING SYSTEMS
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5.0
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Alphabetical group
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Name
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Teaching Assignment Details
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From (included)
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To (excluded)
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A
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ZZZZ
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Loiacono Daniele
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