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

At the end of the course, students will be able to:

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 self-developed project

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.

RECOMMENDED READING/BIBLIOGRAPHY

Materials will be made available on Aulaweb.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/11964/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral exam by appointment.

Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/11964/studenti-disabilita-dsa

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.

FURTHER INFORMATION

For further information, please refer to the course’s AulaWeb module or contact the instructor.