BERGAMO
Overview
Date/time interval
Syllabus
Course Objectives
At the end of the course the student will gain the ability to:
a) choose and apply the appropriate statistical tool, in the class of statistical learning methods, for the analysis of different types of data coming from real-world problems;
b) use the open-source statistical software R (freely available for download at http://www.r-project.org) for performing data analysis and visualization, implementing statistical models and obtaining predictions;
c) interpret the results in a decision making perspective.
Course Prerequisites
- Good knowledge of the fundamentals of Statistics (i.e. descriptive statistics, probability, inferential statistics, linear regression model).
- Basic knowledge of the R programming language.
Teaching Methods
The course consists in theory lectures for a total of 48 hours. Extra hours (usually 16) are dedicated to R lab sessions. The lectures/labs calendar will be published at the beginning of the course on the Moodle page of the course.
Assessment Methods
- a test including open-ended and closed-ended questions concerning theoretical topics or short applications of the studied methods;
- exercises to be solved using the R software in order to evaluate the ability of the student in analysing data and interpreting outputs.
The two parts of the exam (theoretical and practical) are each worth 50% of the total score, approximately.
This course represents the second module of the “CODING AND MACHINE LEARNING” course (12 cfu). The final score will be computed by averaging the grades obtained from the two modules (Coding for Data Science and Machine Learning for Economics). The final scores will be published in the e-learning page of the course.
Contents
- Introduction to machine learning: supervised versus unsupervised learning, the bias-variance trade-off. - Classification methods: K-nearest neighbors classification, logistic regression, naive Bayes, linear and quadratic discriminant analysis, classification trees (including bagging, random forests, boosting). - Regression methods: K-nearest neighbors regression, regression trees (including bagging, random forests, boosting), non-linear regression models (splines, GAM). - Resampling methods: cross-validation and bootstrap. - Unsupervised learning methods: clustering, principal component analysis.
Online Resources
More information
- All the course materials will be made available in the Moodle page.
- Attending lectures and R labs is strongly recommended.