CODE 106734 ACADEMIC YEAR 2025/2026 CREDITS 6 cfu anno 2 BIOENGINEERING 11159 (LM-21) - GENOVA 6 cfu anno 1 BIOENGINEERING 11933 (LM-21 R) - GENOVA SCIENTIFIC DISCIPLINARY SECTOR INF/01 LANGUAGE English TEACHING LOCATION GENOVA SEMESTER 1° Semester TEACHING MATERIALS AULAWEB OVERVIEW This course aims at providing the basics of Artificial Intelligence for biomedical applications. Students will learn machine and deep learning methods and how to set up an experimental pipeline that guarantees reproducible results. All classes are divided into theory and practice. AIMS AND CONTENT LEARNING OUTCOMES Il corso introdurrà i concetti ei principi fondamentali dell'apprendimento automatico e dell'intelligenza artificiale applicata alla medicina. PREREQUISITES Recommended background knowledge Linear algebra Probability and basic statistics Calculus TEACHING METHODS Duration of teaching 48 hours (1/2 theory 1/2 practice). Classes taught in person by the lecturer with the use of slides and/or blackboard. Hands-on practice using Jupyter notebooks. SYLLABUS/CONTENT Introduction to Artificial Intelligence. Examples of AI in medicine and biology. Data driven methods: supervised /unsupervised. Supervised: classification/regression.Unsupervised: clustering and visualization. Bias and Generalization Machine learning methods for prediction. Data: issues and challenges. Methods: examples of predictive models for diagnosis or prognosis. Detection of disease. Disease progression prediction. Disease staging. Regularization methods. Measures to assess the classification model Deep learning methods for prediction. Examples of predictive methods for diagnosis or prognosis. Early detection of disease. Disease progression prediction. Disease staging. Deep learning. Multilayer perceptron. Model dependency on learnable parameter and model stochasticity Applications of AI in medicine. Medical image prediction. Deep learning (CNN). Graph neural networks Examples of variable selection in medicine. Case study: identification of pathogenic molecular variables. SVM-RFE. Lasso and sparsity inducing methods.Variable selection for genomics RECOMMENDED READING/BIBLIOGRAPHY Hastie, Trevor, et al. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. New York: springer, 2009. Hastie, Trevor, Robert Tibshirani, and Martin Wainwright. "Statistical learning with sparsity." Monographs on statistics and applied probability 143 (2015): 143. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. TEACHERS AND EXAM BOARD ANNALISA BARLA Ricevimento: ANNALISA BARLA: on demand, upon explicit request by email LESSONS LESSONS START Classes will start on the last week of September 2022. The schedule of all courses can be found on EasyAcademy. Class schedule The timetable for this course is available here: Portale EasyAcademy
