Multivariate Analysis and Statistical Learning
University of Naples Federico II · B.Sc. in Statistics for Business and Society
Overview
This course provided a first introduction to supervised and unsupervised learning from a statistical perspective.
The emphasis was on the theoretical foundations, with many demonstrations of key results in machine learning and classical multivariate methods.
The course also introduced R programming as a practical tool for implementing models and running analyses, culminating in a final project.
Main objectives
- Understand the basics of supervised learning (e.g., linear models, discriminant analysis, QDA, LDA)
- Explore unsupervised learning (clustering, exploratory data analysis)
- Develop skills in applying theoretical results to practical datasets
- Learn to use R and RStudio for statistical modeling and data exploration
Learning outcomes
- Ability to implement and interpret fundamental machine learning algorithms
- Strong theoretical background with proofs and demonstrations of ML concepts
- First exposure to statistical computing in R
- Capacity to critically evaluate methods through both theory and practice
My work in this course
- Final Project
- Notes & Exercises
- Personal notes with detailed proofs and theoretical derivations in statistical learning