STATISTICAL INFERENCE AND LEARNING

This course belongs to the educational activities of the Master in Computer Science that allow the student to acquire advanced instruments for data analysis and machine learning. The objective of the course is to develop statistical skills for the analysis of high dimensional data and for solving forecasting and classification problems occurring in a wide variety fields, including technological, scientific, biomedical, economic and business fields.

Regular and active participation in the teaching activities offered by the course and in independent research activities will enable students to:
1. (knowledge and understanding)
- know and understand advanced statistical learning methods for synthesis, prediction and classification
2. (applying knowledge and understanding)
- autonomously apply advanced statistical methods to synthetize information, make predictions and classifications even with data characterized by high-dimensionality
- autonomously use statistical software to analyze datasets characterized by high dimensionality
3. (making judgements)
- make autonomous judgements about the validity and feasability of different statistical techniques and understand their impact on the results of the analyses

Students are assumed to have reached the learning objectives of the course Applied Probability for Computer Science (https://www.unive.it/data/insegnamento/335487 ) although it is not formally required to have passed the exam. It is important that the students have a solid familiarity with the basic concepts of probability calculus, random variables, simulation techniques and the basic tools of statistical inference.

The course program includes presentation and discussion of the following topics:
1. linear prediction models
2. classification techniques
3. resampling methods
4. model selection and regularization
5. nonlinear models
Applications with R language (www.r-project.org) are an integral part of the course.

- James G, Witten D, Hastie T, Tibshirani R (2015). An Introduction to Statistical Learning. 6th version. Springer. Webpage http://www-bcf.usc.edu/~gareth/ISL/
- Additional reading and materials distributed during the course through Moodle

The achievement of the course objectives is assessed through a written exam. The exam consists of four exercises designed to measure
1. the theoretical knowledge of the course topics,
2. the ability to apply the knowledge to answer real data problems.
The maximal score for each exercise is 8 points. The final score is the sum of the scores of the four exercises. A total score exceeding 30 corresponds to 30 with honors.

Conventional theoretical lectures complemented by exercise classes, discussion of case studies and computer labs. Teaching material prepared by the lecturer will be distributed during the course through the Moodle platform. The statistical software used in the course is R (www.r-project.org).

English

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