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ARTIFICIAL INTELLIGENCE IN MEDICINE

CODE 106734
ACADEMIC YEAR 2022/2023
CREDITS
  • 6 cfu during the 2nd year of 11159 BIOENGINEERING(LM-21) - 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

    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

    All class schedules are posted on the EasyAcademy portal.