INGLESE

English

Introduction to Modern Statistical Learning Approaches

Linear Regression Model • Using Least Squares to Fit the Model • Testing Statistical Significance • Residual analysis & model checking
• lm() Function to Fit Linear Regression Models in R

Logistic Regression • Using the Logistic Function for Classification • Estimating Regression Coefficients • Estimating Probabilities
Splines & smoothing splines

Linear Discriminant Analysis

Resampling methods: Cross-validation, bootstrap

Regression & classification trees
Bagging, boosting, random forests,
Support vector machines

Introduction to neural networks

 JAMES, WITTEN, HASTIE, TIBSHIRANI. An introduction to statistical learning with applications in R. Springer.
or
 HASTIE, TIBSHIRANI AND FRIEDMAN. The elements of statistical learning: data mining, inference and prediction. Springer-Verlag.

Knowledge and understanding.
The course aims at the introduction and understanding of methodological aspects of Statistical Learning (preliminary concepts)
Applied knowledge and understanding.
The course aims at the knowledge and understanding of the application aspects of the main techniques of Statistical Learning through exercises, laboratory activities and the using of specialist software.
Making judgements
The course aims to give ability to the student at:
- formulating an own evaluation and judgment based on learned notions and from a comparison, in classroom, with the teacher and with the other students;
- identifying and collecting additional information for the subject knowledge through additional books and teaching materials;
- improving ability in how to do and in how to take decisions, considering various aspects of the matter, especially applicative ones;
- performing knowledge extraction from databases by using methodologies and techniques of Statistical Learning with specialist software (R and Python).


Communication skills.
The course aims to provide the student with communication skills on learnt data analysis methods and on results of practical exercises.

Learning skills.
The course aims to provide the student with:
- learning skills necessary for understanding and using of Statistical Learning techniques for data processing;
- ability to draw on different bibliographical sources, in English, in order to acquire new skills in this field.

Basic knowledge of mathematics, descriptive and inferential statistics.

Frontal lessons
Personal study is required on the recommended didactic books
Slides for the course will be provided
Laboratory training

Introduction to Modern Statistical Learning Approaches

Linear Regression Model • Using Least Squares to Fit the Model • Testing Statistical Significance • Residual analysis & model checking
• lm() Function to Fit Linear Regression Models in R

Logistic Regression • Using the Logistic Function for Classification • Estimating Regression Coefficients • Estimating Probabilities
Splines & smoothing splines

Linear Discriminant Analysis

Resampling methods: Cross-validation, bootstrap

Regression & classification trees
Bagging, boosting, random forests,
Support vector machines

Introduction to neural networks