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90477 - Machine Learning Systems For Data Science

Academic Year 2026/2027

                
                        Docente:
                        Stefano Lodi
                    
                        Credits:
                        10
                    
                        SSD:
                        IINF-05/A
                    
                        Language:
                        English
                    
                        Moduli:
                        
                            Stefano Lodi
                            (Modulo 1)
                        
                            (Modulo 2)
                        
                        Teaching Mode:
                        In-person learning (entirely or partially)
                        
                                    In-person learning (entirely or partially) (Modulo 1); 
                                
                                    In-person learning (entirely or partially) (Modulo 2)
                                
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Statistical Sciences (cod. 6810)

                                Also valid for
                                
                                    Second cycle degree programme (LM) in
                                    
                                        Statistical Sciences (cod. 6810)
                                    
                                        Course Timetable
                                    
from Sep 14, 2026 to Oct 15, 2026

                                        Course Timetable
                                    
from Nov 09, 2026 to Dec 16, 2026

Learning outcomes

By the end of the course, the student: -understand the fundamental programming principles of the Python language and be able to apply them primarily to data management and analysis, under the umbrella of data science; -understand the role, purpose and features of Python libraries for numerical computation, data representation, and machine learning, and their interconnectivity with frameworks, such as Jupyter Notebook; -understand the fundamentals of support vector models, neural networks and deep learning algorithms; -is able to apply data science practices and methods to construct models and solve problems for various data-science applications.

Syllabus

The syllabus has not been published yet.

Office hours

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