The objectives of this course are to explore the use of statistical and signal processing applications 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 divided into two parts: 1) the description of some of the many health issues and 2) the description and use of the information and communication techniques that can be used to solve the problems.

Knowledge:
- e-health and m-health applications
- telemedicine applications
- statistical inference techniques
- regression techniques
- classification techniques
- machine learning

Ability:
- to apply regression techniques in health problems
- to apply classification techniques in health problems
- to use open-source machine learning software

Knowledge of probability theory, linear algebra

- Description of some e-health, m-health, and telemedicine applications (1.5 CFU)
- Statistical inference techniques applied to health problems (1CFU)
- Linear regression techniques applied to health problems (1CFU)
- Classification techniques applied to health problems (1 CFU)
- Machine learning applied to health problems (1.5 CFU)

Lectures will describe the health context and the problem to be solved, then the relevant techniques are discussed and used in the laboratory classes. Lab reports are requested.

- Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer-Verlag New York, 2006
- J.Devore, “Probability & Statistics for the Engineers and the Sciences”, Cengage Learning

Each student must write a report on the laboratory experiences, and discuss it during an oral exam. The final grade depends on the completeness and clearness of the report, and on the ability to describe and discuss the results during the oral exam.