MACHINE LEARNING
Stampa
Enrollment year
2021/2022
Academic year
2021/2022
Regulations
DM270
Academic discipline
ING-INF/05 (DATA PROCESSING SYSTEMS)
Department
DEPARTMENT OF ELECTRICAL,COMPUTER AND BIOMEDICAL ENGINEERING
Course
COMPUTER ENGINEERING
Curriculum
Embedded and Control Systems
Year of study
Period
2nd semester (07/03/2022 - 17/06/2022)
ECTS
6
Lesson hours
59 lesson hours
Language
English
Activity type
ORAL TEST
Teacher
CUSANO CLAUDIO (titolare) - 4 ECTS
CUSANO CLAUDIO (titolare) - 2 ECTS
Prerequisites
Students are expected to have a basic knowledge of linear algebra, vector calculus, probability and statistics. They are also expected to be able to design and write simple computer programs.
Learning outcomes
At the end of the course students will be able to understand and discuss the principles of machine learning. They will be able to analyze a problem, and to design and implement a solution. They will be familiar with the most important techniques in the field and will be able to use them to build machine learning systems by using the Python programming language.
Course contents
After a general introduction to machine learning, the first lectures will focus on the main techniques used to tackle the problem of classification by supervised learning. More in detail the following topics will be presented:
- logistic regression;
- generalization and regularization;
- the perceptron algorithm;
- linear and non-linear Support Vector Machines;
- cross validation and model selection;
- feature selection and normalization;
- generative models and naive Bayes.
Artificial neural networks will be the main topic of the second part of the course. The lectures will cover:
- the biological inspiration;
- feed forward networks;
- the backpropagation algorithm;
- introduction to deep learning;
- convolutional neural networks;
- recurrent networks;
- sequence-to-sequence models;
- deep reincorcement learning (introduction).
The last part of the course will present some application domains in which machine learning models are widely used:
- document classification;
- audio processing;
- image recognition.
Teaching methods
About two thirds of the course will be delivered in the form of lectures in which machine learning principles and techniques will be illustrated, also through the presentation of case studies. A third of the course will take place in a laboratory, where students will learn how to solve machine learning problems using the Python programming language.
Reccomended or required readings
The course is based on a set of notes that are supplemented by a selection of articles.
Assessment methods
The exam consists of an interview in which the student will discuss the topics of the course. To assess their capabilities in solving small-scale machine learning problems, students are also required to provide their own solution to a short programming assignment.
Further information
Sustainable development goals - Agenda 2030