OVERVIEW

Machine learning is establishing as a very interesting scientific area thanks to the availability of more and more powerful computers and of algorithms allowing its application to the most diverse sectors, including key sectors for economic and societal development. This course presents the main machine learning methodologies aimed to “pattern recognition”, in particular for the classification of structurally represented data.

AIMS AND CONTENT

LEARNING OUTCOMES

In this module, several Machine learning methods applied to Pattern recognition are presented and - in particular - the case of classification of images from real domains is discussed. Students will learn how to estimate a probability density function using a set of training samples. They will be able to classify samples on the basis of the decision theory and linear and nonlinear classifiers (MDM, k-nn, SVMs, Random forest). They will learn how to design a MLP neural network. They will learn how to assess the accuracy of a supervised classifier. They will also be able to utilize some clustering algorithms (including a fuzzy algorithm) and validate the obtained results.

AIMS AND LEARNING OUTCOMES

Purpose of this teaching unit is to provide the fundamentals of machine learning and to present some advanced methods with special reference to the classification of structurally represented data; in addition, examples will be presented and applications will be discussed related to signal and image classification. Students will learn to represent the features (measures, attributes, characteristics) of a set of samples to be classified by a multidimensional vector space; will be able to reduce the dimensionality of such a representation limiting the information loss, to estimate the probabilistic distributions of data, to classify samples with classical techniques and with more recent ones (SVMs, neural networks, classifier ensembles), evaluate or estimate the accuracy of a supervised classifier, extract the natural classes (clusters) present in a data set, also representing the related uncertainty with the “fuzzy” approach and validating the related results.

PREREQUISITES

Calculus (functions of one or more variables, integrals, functional optimization); probability theory and random variables; matrix calculus.

TEACHING METHODS

Lessons and exercises will all be given in hybrid syncronous modality: in-person lessons in the classroom contemporarily transmitted with Teams.

Students with a certified learning disability (DSA), a disability, or other special educational needs are invited to contact the instructor at the beginning of the course to discuss teaching and examination arrangements that, while respecting the learning objectives of the course, take individual learning needs into account and provide appropriate accommodations.
Please also note that requests for exam accommodations or exemptions must be submitted using the form available at https://modulionline.unige.it/richiesta-adattamenti#no-back, to the course teacher, the DITEN contact person (silvana.dellepiane@unige.it), and the relevant office (inclusione.studenti@info.unige.it) at least seven working days before the examination, in accordance with the guidelines available at https://unige.it/disabilita-dsa/richiesta-servizi

SYLLABUS/CONTENT

In this teaching unit several Machine learning methods applied to pattern recognition are presented and their application to images from real domains are discussed. All these contents will be presented in hybrid syncronous modality (in-person lessons in the classroom contemporarily transmitted with Teams). In particular, the following methods are considered:

• representation of the features of a set of samples to be classified by a multidimensional vector space
• Decision Theory
• Supervised Probability Density Estimation
• Dimensionality Reduction of the Feature Space
• Neural Networks
• Other Linear and Nonlinear Classifiers 
• Accuracy of Supervised Classifiers
• Unsupervised Classifiers (Clustering)
• Fuzzy Sets and Clustering.

RECOMMENDED READING/BIBLIOGRAPHY

Slides presented at lesson will be available in Aulaweb.

Recommended books:

• Duda, R. O., Hart, P. E., Stork, D. G.: 2001, Pattern classification, 2nd ed., Wiley.
• Fukunaga, K.: 1990, Introduction to statistical pattern recognition, 2a ed., Academic Press.
• Bishop C. M.: 2006, Pattern Recognition and Machine Learning, Springer.

More specific references are included in each of the chapters in which the slides are grouped. 

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/corsi/11962/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Mandatory written examination about the topics in the syllabus of the course, with maximum admissible mark equal to 24/30. If a student obtains a sufficient mark in this written exam, then he/she can also optionally take an additional oral examination with maximum admissible mark equal to 30/30 with honors.

ASSESSMENT METHODS

The assessment is based on questions aimed at verifying the understanding of the concepts taught, the ability to choose the most suitable methodology in the presence of specific cases and to apply the algorithms presented in class and during the exercises to simple problems.

FURTHER INFORMATION

Ask the professor for other information not included in the teaching schedule.