Course Details for A.Y. 2017/2018
Name:
Data Driven Decision / Data Driven Decision
Basic information
Credits:
: Master Degree in Computer Science 3 CFU (c)
Degree(s):
Master Degree in Computer Science 2nd anno curriculum NEDAS Compulsory
Language:
English
Course Objectives
The module deals with the main methods for supervised and non-supervised learning. Particular attention will be given to the statistical foundations of learning. The most established techniques to extract information from data to orient decisions will be treated both in their theoretical motivations and in their practical details. Open source tools will support the course step by step, providing continuous verification of the material.
Course Content
- Statistical foundations of learning
- Clustering and other non-supervised methods
- Decision trees - Logic methods
- Support vector machines - Feature selection
- Methods and tools for supervised and non-supervised learning
Learning Outcomes (Dublin Descriptors)
On successful completion of this course, the student should
- know the main aspects and issues related with the content of the course
- know how methods for non supervised learning work
- know how methods for supervised learning work
- know how to identify, among the methods considered, the one most suited for a given problem
- being able to use software system that implement the methods studied
Prerequisites and Learning Activities
Basic programming skills, introductory statistic, linear optimization
Assessment Methods and Criteria
Assignment
Course page updates
This course page is available (with possible updates) also for the following academic years:
To read the current information on this course, if it is still available, go to the university course catalogue .
Course information last updated on: 03 gennaio 2017, 17:17