Teaching code

4S008979

Academic staff

Catia Scricciolo, Claudia Di Caterina

Coordinator

Catia Scricciolo

Credits

6

Language

English

Scientific Disciplinary Sector (SSD)

SECS-S/01 - STATISTICS

Period

Secondo semestre (lauree magistrali) dal Feb 20, 2023 al May 19, 2023.

Lessons timetable Moodle Seminars 0

Learning objectives

The goal of the course is to provide students with mathematical, statistical and computational tools for a rigorous understanding of machine learning. A central aspect is the critical discussion of how and to which extent machine learning methods are essential in large scale data analysis in order to develop a professional profile combining solid quantitative skills with an in-depth knowledge of economic and corporate dynamics to support strategic decisions based on data analysis. At the end of the course students will be able to master classical methods of machine learning, implement data analysis algorithms, choose the most suitable techniques, identify relevant structures underlying the data for prediction purposes, critically discuss the output generated by a machine learning technique.

Prerequisites and basic notions

The knowledge provided in the basic courses of statistics and econometrics is assumed.

Program

- Overview of Statistical Learning
- Linear Regression Models and Least Squares
• The Gauss-Markov Theorem
• Best-Subset Selection
• Shrinkage Methods: Ridge Regression and the Lasso
- Linear Methods for Classification
• Bayes classifier
• Linear Discriminant Analysis
• Logistic Regression
- Model Assessment and Selection
• Bias-Variance and Model Complexity
• Cross-Validation
- Introduction to Neural Networks
• Neural Networks
• Fitting Neural Networks
- Clustering Methods

Bibliography

Didactic methods

The course includes 36 hours of frontal teaching, of which 24 hours of lessons (equal to 4 CFU) and 12 hours of lab sessions (equal to 2 CFU).

Learning assessment procedures

The exam will test for
(a) the understanding of the theoretical tools (concepts and formal models) presented in the course,
(b) the ability to use theoretical tools to discuss results from a data set analysis.
The final exam will consist of two parts:
- a written exam on the material of the theoretical lectures and the lab sessions. At the end of the course one general
assignment will be given to be delivered before the oral exam on a date to be communicated later on,
- an oral test on the theoretical lectures of the course.

Evaluation criteria

The theory part of the written test has a weight of 2/3, while the part on the use of the software has a weight of 1/3.

Criteria for the composition of the final grade

The final grade of the exam results from the arithmetic average of the marks of the written and oral tests.

Exam language

Inglese