The medium of instruction is English. The objectives of this course are to use machine learning in public health applications, in particular in the areas of basic research, prevention, diagnostic process, management of elderly people at home. The course is designed jointly with the course "Statistical Learning and Neural Networks", with the objective to provide students with a coordinated "machine learning" approach that can be applied to several ICT problems; in particular, "Statistical Learning and Neural Networks" deals primarily with machine learning in terms of classification and neural networks, while "ICT for health" addresses regression and clustering topics. Some classification techniques not analyzed in "Statistical Learning and Neural Networks" are analyzed in "ICT for health". The course is divided into two parts: 1) the description of some of the many health issues and 2) the description and use of the machine learning techniques that can be used to solve these issues. Several laboratory experiences are included, and the knowledge of the health issues from the medical point of view is fundamental for the correct system implementation. Python will be used as programming language (in particular Pandas and Scikit-learn ) and a learn-by-doing approach will be used.

Knowledge of: - basics in some health issues (management of elderlies, Parkinson's disease, EEG, ECG, dermatology, etc) - e-health and m-health applications - telemedicine applications - regression techniques - clustering techniques - classification techniques Ability to: - understand the issues of a telemedicine application - apply regression techniques in health problems - apply clustering techniques in health problems - apply classification techniques in health problems - use open-source machine learning software

Knowledge of probability theory, linear algebra, optimization techniques

- Description of some e-health, m-health, and telemedicine applications (2 CFU) on the following topics: smart aging, fitness, Parkinson's disease, EEG, ECG, dermatology, lean in health care (2.1 CFU). - Review of linear algebra and basics on optimization methods (0.6 CFU). - Introduction to Python (0.3 CFU). - regression techniques: linear regression, PCR, Gaussian processes for regression (0.9 CFU) - clustering techniques: k-means, hierarchical trees, DBSCAN (0.9 CFU) - classification techniques: decision trees and information theory, Hidden Markov Models (0.9 CFU) - Independent component analysis (ICA) applied to EEG (0.3 CFU)

Lectures will describe the health context and the problem to be solved, then the relevant ICT/learning machine methods to be used to solve the problem are discussed and implemented in Python in the laboratory classes.

- Class slides will be available on the portal - 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

Modalitą di esame: Prova orale obbligatoria; Elaborato scritto individuale; Elaborato progettuale individuale;

The student must report the lab activities: for each lab he/she must upload the report (already partially available, to be completed), and the Python scripts (partially available, to be completed). The maximum grade for the lab activity is 10, depending on the correctness of the results and on the completeness and clearness of the document. The mandatory oral exam consists of 3 questions, 1 question about the health issues, i.e. Parkinson's disease, dermatology, etc. as described in the lectures, 2 questions about the algorithms and methods described in the lectures and the lab activity. The student gets a grade from 0 to 7 on each question; the grade depends on the ability to describe and critically discuss the learned methods and on the promptness of the answers. The grades of the reports and the oral exam are added together to obtain the final grade. The "lode" is given to students with an overall grade 31. The ability of the student to apply the described machine learning techniques in Python will be checked through the analysis of the report and the Python scripts. The knowledge of the health issues with possible ICT solutions and the knowledge of the regression, clustering and classification techniques will be checked during the oral exam. The student will improve his/her soft-skills related to the ability of writing a technical report, and the ability to discuss ideas during the oral exam.

Modalitą di esame: Prova orale obbligatoria; Elaborato scritto individuale; Elaborato progettuale individuale;

The student must report the lab activities: for each lab he/she must upload the report (already partially available, to be completed), and the Python scripts (partially available, to be completed). The maximum grade for the lab activity is 10, depending on the correctness of the results and on the completeness and clearness of the document. The mandatory oral exam consists of 3 questions, 1 question about the health issues, i.e. Parkinson's disease, dermatology, etc. as described in the lectures, 2 questions about the algorithms and methods described in the lectures and the lab activity. The student gets a grade from 0 to 7 on each question; the grade depends on the ability to describe and critically discuss the learned methods and on the promptness of the answers. The grades of the reports and the oral exam are added together to obtain the final grade. The "lode" is given to students with an overall grade 31. The ability of the student to apply the described machine learning techniques in Python will be checked through the analysis of the report and the Python scripts. The knowledge of the health issues with possible ICT solutions and the knowledge of the regression, clustering and classification techniques will be checked during the oral exam. The student will improve his/her soft-skills related to the ability of writing a technical report, and the ability to discuss ideas during the oral exam.