OVERVIEW

In the information age any system or device generates some form of data for diagnostic purposes or analysis.
he course details the techniques for analyzing data in order to extract useful information and knowledge for decision making.

AIMS AND CONTENT

LEARNING OUTCOMES

Students will be provided with advanced skills related to machine learning and data analysis.
Students will learn insights on machine learning and data analysis methodologies and a series of real world applications.

AIMS AND LEARNING OUTCOMES

The student will be able to apply the acquired skills to a case study by deriving the model of the phenomenon that generated the data under analysis.

PREREQUISITES

Coding (Matlab/Python/R), linear algebra, probability and statistics.

TEACHING METHODS

The course consists of lectures and practical lab sessions using Matlab/Python/R

SYLLABUS/CONTENT

1. Statistical inference
2. Supervised, Semisupervised, and Unsupervised Learning
3. Statistical Learning Theory
4. Shallow Machine Learning Algorithms (examples of coding in Matlab/Python/R)
5. Deep Machine Learning Algorithms (examples of coding in Matlab/Python/R)
6. Model Selection and Error Estimation

RECOMMENDED READING/BIBLIOGRAPHY

C. C. Aggarwal "Data Mining - The textbook" 2015
T. Hastie, R.Tibshirani, J.Friedman "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009.
S. Shalev-Shwartz, S. Ben-David "Understanding machine learning: From theory to algorithms" 2014
I. Goodfellow, Y. Bengio, A. Courville "Deep learning" 2016
L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020

TEACHERS AND EXAM BOARD

Exam Board

LUCA ONETO (President)

MARCO MARATEA

DAVIDE ANGUITA (President Substitute)

LESSONS

LESSONS START

https://courses.unige.it/11160/p/students-timetable

EXAMS

EXAM DESCRIPTION

Oral by appointment

ASSESSMENT METHODS

The student will solve a real problem at will by applying the techniques learned during the course.

Exam schedule