Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence

A.Y. 2019/2020
Lesson for
12
Max ECTS
80
Overall hours
SSD
INF/01 SECS-S/01
Language
English
Learning objectives
The aim of the course is to introduce students to algorithms and statistical tools which are frequently applied in machine learning context.

During the course some lectures will be devoted to explain the different techniques from a theoretical point of view, to enforce the comprehension of the subject. In addition, some laboratories will be held to develop the skills for using a software and for providing statistical interpretations of the outputs.

At the end of the course a student will be able to solve a practical problem by applying algorithms and analyzing data sets.

Course structure and Syllabus

Active edition
Yes
Module Machine Learning
INF/01 - INFORMATICS - University credits: 6
Lessons: 40 hours
Module Statistical Learning, Deep Learning and Artificial Intellingence
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
ATTENDING STUDENTS
Module Statistical Learning, Deep Learning and Artificial Intellingence
Syllabus
Regression analysis
Regularization methods: Lasso, Lars, Elastic Net
Classification methods
Tree-based methods (cart, random forests, etc)
Dimensionality reduction methods
Neural-Networks
Deep-Learning
Bayesian learning and principles of Artifical Intelligence.
NON-ATTENDING STUDENTS
Module Statistical Learning, Deep Learning and Artificial Intellingence
Syllabus
Regression analysis
Regularization methods: Lasso, Lars, Elastic Net
Classification methods
Tree-based methods (cart, random forests, etc)
Dimensionality reduction methods
Neural-Networks
Deep-Learning
Bayesian learning and principles of Artifical Intelligence.
Lesson period
Second semester
Lesson period
Second semester
Assessment methods
Esame
Assessment result
voto verbalizzato in trentesimi
Professor(s)
Reception:
Wednesday 9:30AM-12:30PM
39, via Comelico. Room P101