Provided by:
S.ANNA
From:
Altro PhD (Economics)
Lecturers:
Francesca CHIAROMONTE
Semester:
2
Hours:
30
Exam:
Y
Educational Goals:
This course introduces the students to a number of topics in contemporary Statistical Learning, including: (i) Unsupervised classification and clustering methods. (ii) Unsupervised dimension reduction; Principal Components Analysis and related techniques. (iii) Supervised classification methods. (iv) Linear and generalized linear models. (v) Non-parametric regression methods. (vi) Resampling methods, Cross Validation, the Bootstrap and permutation-based techniques. (vii) Feature selection and penalized fitting techniques for (generalized) linear models (viii) Supervised dimension reduction; Sufficient Dimension Reduction and related techniques. Compared to traditional courses on regression, linear models and multivariate statistics, we focus on analyzing actual datasets of interest to the students through group projects, and we adopt a so-called active learning approach, leveraging practicum sessions and publicly available MOOC materials.
Prerequisites:
A working knowledge of basic statistical inference procedures (point estimation, confidence intervals, testing) and regression modeling.
