ID:

SCV0631

Durata (ore):

###### 72

CFU:

9

SSD:

###### SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI

Anno:

2024

## Dati Generali

## Periodo di attività

Primo Semestre (23/09/2024 - 20/12/2024)

## Syllabus

## Obiettivi Formativi

The student will be able to:

1. Know the main objectives and areas of Artificial Intelligence, Machine Learning, and Pattern Recognition, with the ability to identify the potentialities of intelligent techniques and the relationships with other disciplines

2. Know the basic concepts of automated learning based on machine learning approaches and the conditions for their applicability

3. Know the most relevant feature extraction and selection techniques

4. Know statistical techniques and their limitations and strengths, with the ability to appropriately select the proper technique in specific contexts

5. Know basic principles of neural computing and their characteristics

6. Know Flat and Hierarchical Clustering with the ability to configure and apply these methods in specific contexts

7. Know performance metrics for learners

8. Know basic concepts of the following application domains: Image Classification, Text Categorization, Biomedical Data Analysis

9. Know how to program in a language for statistical computing and machine learning applications like R

It is also expected that students develop communicative skills through open discussion and autonomous assessment in the choice of the proper technique to solve problems of recognition and /or automatic classification of multidimensional data in several domains.

Students will acquire also knowledge of the relevant Machine learning and Pattern Recognition terminology.

## Prerequisiti

## Metodi didattici

The topics of the course are illustrated by means of (1) conceptual, formal descriptions, (2) their implementation via R code, and (3) the use of demos and online resources.

Constant interaction with the students and their involvement in open discussions are highly encouraged.

## Verifica Apprendimento

The goal of the written test is to assess the learning degree and the understanding of the elements related to intelligent systems from both theoretical and application (on problems of limited complexity) points of view. Written tests normally consist of

- two exercises for the assessment of the student's understanding and knowledge of machine learning techniques: each exercise weighs about one-quarter of the grade of the written exam;

- four questions on the conceptual aspects: each exercise weighs about one-eighth of the grade of the written exam.

The assignment allows the students to use their skills and knowledge for the building and evaluation of machine learners by using the R language. The project presentation has the goal of assessing the students’ communication skills in two areas: 1) the students’ technical competencies and use of the correct terminology and 2) the students’ skills for communicating a complete and organized view of the work they carried out.

Individual judgment skills are evaluated based on the decisions made during the written exam and the assignment.

The grade of the written test is on a 0 to 30 scale. The written exam contributes 70% of the final mark, while the assignment accounts for the remaining 30%.

## Contenuti

1) Introduction to Artificial Intelligence and Pattern Recognition: Historical Perspective, State of the Art of Methods and Applications (3 h - Course Objective 1)

2) Basic Mathematical and Statistical Concepts:

• Measurement Theory

• Matrix algebra

• Multivariable function analysis calculus: partial derivatives and gradient

• Relevant concepts of Probability and Statistics

• Relevant concepts of Information Theory

(6 h - Course Objective 2)

3) Design of a Supervised Classifier; Basic principles of learning by example; basic concepts of multidimensional pattern analysis

(5 h - Course Objective 2)

4) Feature Extraction and Selection:

• Principal Component Analysis

• Information Gain

• Statistical evaluation of features

• Selection Strategies

(6 h - Course Objective 3)

5) Fundamental Elements of Programming with R

(8 h - Course Objective 9)

6) Machine Learning Algorithms

• Ordinary Least Squares Regression

• Outliers and Robust Regression

• Regularization and Shrinkage in Regression: Ridge Regression, LASSO Regression, Elastic Net

• Minimum distance classifier

• Bayesian classification

• Maximum likelihood classifier

• K-Nearest Neighborhood classifier

• Parallelepiped Method

• Decision trees

• Ensemble models: boosting, bagging, stacking

• Support Vector Machine

• Imbalance, Hyperparameter tuning

• Performance metrics

• (32 h - Course Objectives 2, 3, 4, 7)

7) Neural Networks

• Introduction, taxonomy

• Basic principle of neural computing

• Feedforward Neural Models

• Application Examples

• Introduction to Deep Learning

(6 h - Course Objective 5)

8) Clustering

• Introduction to Clustering

• K-means Clustering algorithm

• Agglomerative Hierarchical Clustering: Single linkage, Complete linkage

(3 h - Course Objective 6)

9) Design of Intelligent Systems, Examples in Application Domains

(3 h - Course Objectives 1, 8)

## Lingua Insegnamento

INGLESE

## Altre informazioni

## Corsi

## Corsi

##### INFORMATICA

Laurea Magistrale

2 anni

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