Course Syllabus
Obiettivi
The course covers a set of tools for modelling and understanding complex datasets behind the Statistical Learning world. Statistical Learning has become a very hot field in many scientific areas as well as marketing, finance, and other business disciplines.
At the end of the course, students will be able to properly use Statistical Learning techniques to analyse their data.
Contenuti sintetici
The course deals with Statistical Learning techniques for handling specific data's issue such as local regression, tree-based methods, and support vector machine. Additionally, the course introduces to the students the R software.
Programma esteso
- Introduction to Statistical Learning
- Statistical Learning in High Dimensions
- Moving beyond linearity: regression and smoothing splines, local regression and generalized additive models
- Tree-based methods: decision trees, bagging, random forests, boosting, and Bayesian additive regression trees
- Statistical learning with the software R
Prerequisiti
Advanced Foundations of Statistics for AI and Advanced Foundations of Mathematics for AI
Modalità didattica
Lessons are held both in classroom and in lab, integrating theoretical principles with practicals of data analysis and programming in R.
Materiale didattico
Gareth J., Witten D., Hastie T., Tibshirani R., An Introduction to statistical learning with application in R, springer, second edition (2021) – book available on-line at https://www.statlearning.com/.
Kuhn, Johnson (2019). Feature Engineering and Selection. Chapman and Hall/CRC – online version of the book at http://www.feat.engineering/.
If necessary, research documents/papers will be also provided during the course.
Modalità di verifica del profitto e valutazione
Written exam that aims at verifying both theoretical and practical acquired knowledge. The oral exam is optional; if requested (by the student or by the teacher), the final mark is obtained by averaging written and oral marks.
Orario di ricevimento
Students that would like to fix an appointment with the Professor should send an email to matteo.borrotti@unimib. The appointment can be arranged both in presence or on-line.
Aims
The course covers a set of tools for modelling and understanding complex datasets behind the Statistical Learning world. Statistical Learning has become a very hot field in many scientific areas as well as marketing, finance, and other business disciplines.
At the end of the course, students will be able to properly use Statistical Learning techniques to analyse their data.
Contents
The course deals with Statistical Learning techniques for handling specific data's issue such as local regression, tree-based methods, and support vector machine. Additionally, the course introduces to the students the R software.
Detailed program
- Introduction to Statistical Learning
- Statistical Learning in High Dimensions
- Moving beyond linearity: regression and smoothing splines, local regression and generalized additive models
- Tree-based methods: decision trees, bagging, random forests, boosting, and Bayesian additive regression trees
- Statistical learning with the software R
Prerequisites
Advanced Foundations of Statistics for AI and Advanced Foundations of Mathematics for AI
Teaching form
Lessons are held both in classroom and in lab, integrating theoretical principles with practicals of data analysis and programming in R.
Textbook and teaching resource
Gareth J., Witten D., Hastie T., Tibshirani R., An Introduction to statistical learning with application in R, springer, second edition (2021) – book available on-line at https://www.statlearning.com/.
Kuhn, Johnson (2019). Feature Engineering and Selection. Chapman and Hall/CRC – online version of the book at http://www.feat.engineering/.
If necessary, research documents/papers will be also provided during the course.
Semester
Second
Assessment method
Written exam that aims at verifying both theoretical and practical acquired knowledge. The oral exam is optional; if requested (by the student or by the teacher), the final mark is obtained by averaging written and oral marks.
Office hours
Students that would like to fix an appointment with the Professor should send an email to matteo.borrotti@unimib. The appointment can be arranged both in presence or on-line.
Key information
Staff
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Matteo Borrotti