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ADVANCED TOPICS IN STATISTICS Single discipline educational activity
Course Sheet Academic Year of enrolment:
Professor and Collaborators:
Hours of classroom activity:
Prerequisites:
Mathematics (calculus), linear algebra and statistical inference (estimation and statistical test), Data Mining
Objectives
Contents The following topics are considered as important parts of the teaching program for the fulfilment of the objectives: data visualization of complex data, non-parametric regression, dependence analysis of complex data (non-linear), text mining
Extended Syllabus Topics of interest for the project will be chosen within trhe contents of the following book
Kevin Murphy (2012) Machine learning : a probabilistic perspective, The MIT Press, Cambridge, Massachusetts, London, England
Recommended Bibliography Kevin Murphy (2012) Machine learning : a probabilistic perspective, The MIT Press, Cambridge, Massachusetts, London, England
Teaching Methods Weekly meetings for the development of a project using advanced statistical tecniques for the analysis of complex data
Evaluation methods Verification of learning:
The exam consists in discussing (60 minutes) the project based on the statistical analysis of data sets chosen together with the teacher. The final grade will be given in thirtieths.
Contacts/More Information E-mail: ippoliti@unich.it
Students will be received on Mon and Wed between 15:00 and 16:00; Appointments can be fixed by e-mail.