The course focuses on the study of fundamental concepts of machine learning, the main machine learning methods and software tools based on machine learning techniques. In particular, it aims at providing an understanding of the capabilities and limitations of these methods and tools, and to develop the ability to use some of the currently available technologies to solve problems of data analysis, knowledge extraction and decision support. Finally, it aims at discussing the main problems and opportunities that arise in contexts where the analysis extends to large volumes of data (Big Data).
Curriculum
teacher profile teaching materials
Introduction to machine learning
Types of machine learning
Methods for supervised and unsupervised learning
Model training and inference
Technologies and software tools for machine learning
Use of software tools for machine learning
Application of machine learning models with different types of datasets
Mutuazione: 21210492 Tecnologie per il machine learning e big data in Economia e Gestione della Trasformazione Digitale LM-56 R DI SANZO PIERANGELO
Programme
Introduction to artificial intelligenceIntroduction to machine learning
Types of machine learning
Methods for supervised and unsupervised learning
Model training and inference
Technologies and software tools for machine learning
Use of software tools for machine learning
Application of machine learning models with different types of datasets
Prerequisiti
Basic ability to use software tools for processing data in csv/dsv tabular format, for example spreadsheets. Knowledge of a programming language like Python is not required but may enable more effective use of certain software tools.Core Documentation
Course materials provided by the teacher and made available on the course webpage on the university's e-learning systemAttendance
Attendance is not mandatory, but it is recommended especially to facilitate learning how to use the software tools.Type of evaluation
The examination includes a computer-based test and the evaluation of a project teacher profile teaching materials
Introduction to machine learning
Types of machine learning
Methods for supervised and unsupervised learning
Model training and inference
Technologies and software tools for machine learning
Use of software tools for machine learning
Application of machine learning models with different types of datasets
Mutuazione: 21210492 Tecnologie per il machine learning e big data in Economia e Gestione della Trasformazione Digitale LM-56 R DI SANZO PIERANGELO
Programme
Introduction to artificial intelligenceIntroduction to machine learning
Types of machine learning
Methods for supervised and unsupervised learning
Model training and inference
Technologies and software tools for machine learning
Use of software tools for machine learning
Application of machine learning models with different types of datasets
Prerequisiti
Basic ability to use software tools for processing data in csv/dsv tabular format, for example spreadsheets. Knowledge of a programming language like Python is not required but may enable more effective use of certain software tools.Core Documentation
Course materials provided by the teacher and made available on the course webpage on the university's e-learning systemAttendance
Attendance is not mandatory, but it is recommended especially to facilitate learning how to use the software tools.Type of evaluation
The examination includes a computer-based test and the evaluation of a project teacher profile teaching materials
Introduction to machine learning
Types of machine learning
Methods for supervised and unsupervised learning
Model training and inference
Technologies and software tools for machine learning
Use of software tools for machine learning
Application of machine learning models with different types of datasets
Mutuazione: 21210492 Tecnologie per il machine learning e big data in Economia e Gestione della Trasformazione Digitale LM-56 R DI SANZO PIERANGELO
Programme
Introduction to artificial intelligenceIntroduction to machine learning
Types of machine learning
Methods for supervised and unsupervised learning
Model training and inference
Technologies and software tools for machine learning
Use of software tools for machine learning
Application of machine learning models with different types of datasets
Prerequisiti
Basic ability to use software tools for processing data in csv/dsv tabular format, for example spreadsheets. Knowledge of a programming language like Python is not required but may enable more effective use of certain software tools.Core Documentation
Course materials provided by the teacher and made available on the course webpage on the university's e-learning systemAttendance
Attendance is not mandatory, but it is recommended especially to facilitate learning how to use the software tools.Type of evaluation
The examination includes a computer-based test and the evaluation of a project teacher profile teaching materials
Introduction to machine learning
Types of machine learning
Methods for supervised and unsupervised learning
Model training and inference
Technologies and software tools for machine learning
Use of software tools for machine learning
Application of machine learning models with different types of datasets
Mutuazione: 21210492 Tecnologie per il machine learning e big data in Economia e Gestione della Trasformazione Digitale LM-56 R DI SANZO PIERANGELO
Programme
Introduction to artificial intelligenceIntroduction to machine learning
Types of machine learning
Methods for supervised and unsupervised learning
Model training and inference
Technologies and software tools for machine learning
Use of software tools for machine learning
Application of machine learning models with different types of datasets
Prerequisiti
Basic ability to use software tools for processing data in csv/dsv tabular format, for example spreadsheets. Knowledge of a programming language like Python is not required but may enable more effective use of certain software tools.Core Documentation
Course materials provided by the teacher and made available on the course webpage on the university's e-learning systemAttendance
Attendance is not mandatory, but it is recommended especially to facilitate learning how to use the software tools.Type of evaluation
The examination includes a computer-based test and the evaluation of a project