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.

Basic knowledge of probability theory, linear algebra, optimization techniques, programming.

- 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/teledermatology - lean in health care - management of emergencies - differences among AI (Artificial Intelligence), ML (Machine Learning) and Deep Learning.. - Review of linear algebra and basics on optimization methods (0.6 CFU). - Introduction to Python (0.3 CFU). - Regression techniques: linear regression and tests of Gaussianity, applied to score of 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. Questions on the lab activities might 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 mandatory oral exam for this course comprises three questions. The first question focuses on health issues covered in the lectures, such as Parkinson's disease, dermatology, and others. The second and third questions pertain to the algorithms and methods discussed in the lectures and applied in the lab activities. The 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 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. Access to the oral exam is possible only for those students who pass a closed book short quiz (15 minutes) with 9 multiple choice questions (at least 5 out of 9 points, wrong answers give a negative contribution), administered through the PoliTO Exam platform, the same day as the oral exam, same room. The quiz grade is not used for the final grade.

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.