In this course, you will develop the following abilities: Understand the issues related to an e-health application. Explain the differences among AI, ML, Deep Learning. Explain the regression technique covered in the lectures and apply them to health problems. Explain the clustering techniques discussed in the lectures and apply them to health problems. Explain the classification techniques presented in the lectures and apply them to health problems. Compare the various clustering techniques described and choose the most appropriate one for a given problem. Compare the different classification techniques described and determine the best one to address a given problem. By the end of the course, you will have the knowledge and skills necessary to understand e-health applications, apply regression, clustering, and classification techniques to health-related problems, compare and select the most suitable techniques for specific problems.

Knowledge of probability theory: - axioms of probability, conditional and joint probability, Bayes theorem/rule, - statistical independence, correlation, - probability density function (pdf) and cumulative distribution function (CDF), - vectors of random variables and their pdf and CDF, - Gaussian random variable and vector of Gaussian random variables (uncorrelated and correlated). Knowledge of linear algebra: - vectors and matrices, manipulation of matrices (sum, product, transpose, inverse, etc.) and vectors (inner product, square norm) - eigenvalues and eigenvectors, eigendecomposition, Knowledge of optimization techniques: - minimum and maximum of a function of one variable, Taylor expansion, - gradient, Hessian, minimum and maximum of a function of more than one variable, Taylor expansion of a function of more than one variable Knowledge of programming: - for, while loops - if clause - implementation and call of of a function/method/subroutine

- Description of some e-health, m-health, and telemedicine applications (2.1 CFU) on the following topics: - smart aging - fitness - Parkinson's disease - EEG - ECG - dermatology/tele-dermatology - lean in health care - management of emergencies - differences among AI (Artificial Intelligence), ML (Machine Learning) and Deep Learning.. - Review of linear algebra and optimization methods (0.6 CFU). - Introduction to Python (0.3 CFU). - Regression techniques: linear regression and tests of Gaussianity, applied to Parkinson's disease (0.9 CFU) - Clustering techniques: k-means, hierarchical trees, and DBSCAN, applied to body images like CTScan or pictures of skin moles (0.9 CFU) - Classification techniques: sensitivity, specificity, prevalence, incidence applied to tests based on blood markers; decision trees and information theory applied to detection of an illness like Chronic Kidney Disease (0.9 CFU) - Independent component analysis (ICA) applied to EEG (0.3 CFU).

During the lectures, the course will provide descriptions of the health context and the specific problems that need to be addressed (20 hours). Subsequently, the relevant ICT methods to solve these problems will be discussed (20 hours), and practical implementation using Python will be carried out in the laboratory classes (20 hours). While no formal report on the lab activities is required, it is crucial to actively participate in the lab exercises as they are fundamental for understanding and correctly applying the methods explained in the lectures. Students have the freedom to work in groups during the lab sessions, encouraging collaborative learning and problem-solving. A question on the lab activities will be asked at the exam.

Reference material: class slides for both lectures and laboratories. Additional material: - K. Murphy, "Machine Learning, a probabilistic perspective", MIT press, 2012 - Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer-Verlag New York, 2006 - David J.C. MacKay, "Information Theory, Inference and Learning Algorithms" Cambridge University Press 2003 - C.E. Rasmussen, C.K.I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006

Lecture slides; Lab exercises; Video lectures (previous years);

Exam: Compulsory oral exam; Computer-based written test in class using POLITO platform;

The exam is made of two parts: a written quiz and an oral part. The written quiz is closed book with 9 simple multiple choice questions to answer in 15 minutes administered through PoliTO Exam platform. Correct answers increase the grade by 1 point, wrong answers decrease the grade by 0.25 points. It is mandatory to have a grade at least equal to 5 in the written quiz to access the oral part. The grade of the written part is not included in the final grade (the quiz is only used to access the oral part; the final grade reflects the student performance in the oral exam only). The mandatory oral exam comprises three questions. The first one focuses on health issues covered in the lectures, such as Parkinson's disease, dermatology, and others. The second one pertains to the algorithms and methods discussed in the lectures and applied in the lab activities. The third one is about the laboratory activity (run, highlight the portion of the software that solves a specific problem, comment the results). Questions will check that the student has acquired the abilities described in the expected learning outcome section (ability to explain, to compare, to understand). The oral exam evaluates the student's understanding of health issues and potential solutions, as well as their grasp of regression, clustering, and classification techniques. Furthermore, the exam aims to improve the student's soft skills, particularly their ability to engage in discussions, articulate ideas, and describe methods effectively. Each question of the oral exam is graded on a scale of 0 to 10, considering factors such as the accuracy of the answer, response time, and the student's ability to critically discuss the learned methods. The "lode" distinction is awarded to students who achieve a grade of 30 and demonstrate exceptional confidence in their knowledge, promptness in answering questions, clarity in their responses, and the ability to apply learned methods to new scenarios.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.