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.
The course is designed to equip students with advanced knowledge and skills in the fields of machine learning and data analysis. Building upon foundational concepts, students delve into cutting-edge techniques and methodologies essential for tackling real-world problems in diverse domains. The course addresses a comprehensive review of fundamental machine learning algorithms, including supervised and unsupervised learning, and deep learning architectures. Through hands-on exercises and projects, students gain proficiency in implementing these algorithms using popular libraries.
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.
During the course the following skills will be developed - personal competence - social competence - ability to learn to learn - competence in project creation - competence in project management
Coding (Matlab/Python/R), linear algebra, probability and statistics.
- Frontal lesson (approx. 50% to develop ability to learn to learn) - Laboratories (approx. 50% to develop personal competence) - Possibility of a final project in pairs (to develop social competence, competence in project creation, and competence in project management)
For working students and students with certification of Specific Learning Disabilities (SLD), disabilities, or other special educational needs are advised to contact the instructor at the beginning of the course to arrange teaching and examination methods that, while respecting the teaching objectives, take into account individual learning styles.
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 C. M. Bishop, H. Bishop. Deep learning: Foundations and concepts. Springer Nature, 2023. L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020
Ricevimento: By appointment, scheduled by email.
Ricevimento: By appointment.
LUCA ONETO (President)
FABIO ROLI
DAVIDE ANGUITA (President Substitute)
https://easyacademy.unige.it/portalestudenti/index.php?view=easycourse&_lang=it&include=corso
Oral by appointment.
The student will solve a real problem at will by applying the techniques learned during the course.
