Topics in Statistical Learning

Provided by: 

S.ANNA

From: 

Altro PhD (Economics)

Lecturers: 

Francesca CHIAROMONTE

Semester: 

2

Hours: 

30

Exam: 

Y

Educational Goals: 

This course will introduce the students to a number of topics in contemporary Statistical Learning selected from the following list: (i) Unsupervised classification; 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, the focus will be on analyzing actual datasets of interest to the students through group projects. We will also attempt to utilize a so-called active learning approach, leveraging publicly available MOOC materials.

Prerequisites: 

A working knowledge of basic statistical inference procedures (point estimation, confidence intervals, testing) and regression modeling.
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