OVERVIEW

The course encourages students to apply machine learning, distributed computing, and data warehousing methods, algorithms, and technologies in a predictive analytics project, on which students will work on their own.

AIMS AND CONTENT

LEARNING OUTCOMES

Learning how to work on a predicted analytics project, relying on machine learning, distributed computing, and data warehousing methods, algorithms, and technologies.

AIMS AND LEARNING OUTCOMES

APPLY machine learning, distributed computing, and data warehousing methods, algorithms, and technologies. methods, algorithms, and technologies on a real predictive analytics project.

PREREQUISITES

Basics of Machine Learning, Distributed Computing, Data Warehousing

TEACHING METHODS

Class and outside preparation

SYLLABUS/CONTENT

Depending on the courses you passed in the first term, you are either free to work on a predictive analytcs project of your choice or you will work on a project assigned by one of the instructors.

TEACHERS AND EXAM BOARD

Exam Board

ALESSANDRO VERRI (President)

GIOVANNA GUERRINI

BARBARA CATANIA (President Substitute)

LESSONS

LESSONS START

In agreement with the calendar approved by the Degree Program Board of Computer Science.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

ASSESSMENT METHODS

Through an autonomous project, we will check the student ability to combine and apply what they learnt in the Machine Learning, Distributed Computing, and Data Wareousing courses on a concrete predictive analytics project.

Exam schedule