The objectives of this course are to explore the use of data science and machine learning in the
public health field, in particular in the areas of basic research, prevention, diagnostic process, management of eldery people at home. The course is designed jointly with the course "Statistical Signal Processing", with the objective to provide students with a coordinated "machine learning" approach that can be applied to several ICT problems; in particular, "Statistical Signal Processing" deals primarily with machine learning basics, classification and neural networks, while "ICT for health" addresses regression and clustering topics. Some classification techniques not analyzed in "Statistical Signal Processing" are however 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 data science techniques that can be used to solve these issues.

Knowledge of:
- e-health and m-health applications
- telemedicine applications
- regression techniques
- clustering techniques
- classification techniques

Ability to:
- 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

- Description of some e-health, m-health, and telemedicine applications (2 CFU)
- regression techniques: linear and nonlinear, Gaussian processes (1CFU)
- clustering techniques: k-means, hierarchical trees (1CFU)
- classification techniques: decision trees, SVM, Hidden Markov Models (1.5 CFU)
- Independent component analysis (ICA) applied to EEG (0.5 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 the laboratory classes. For each lab a report is required, and in the end all the reports must be revised and collected into a final report that must include comments and comparisons among the used techniques.

- 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

Each student must write a final report on the laboratory experiences, and discuss it during an oral exam (half an hour). The final report must be sent via email to the professor at least three working days before the oral exam.
The final grade depends on the completeness and clearness of the report (70%), and on the ability to describe and critically discuss the obtained results during the oral exam (30%). The maximum grade is 30 lode.