ITALIANO

Approfondimento su argomenti di Ingegneria del Software, in particolare metodologie Agili. Metodologie di sviluppo a Microservizi e uso dei Container.
Introduzione al Machine Learning con approfondimenti sulle principali tecniche di Classificazione e Regressione. Reti neurali e cenni sul Deep Learning

Design Patterns, Gamma, Helm, Johnson, Vlissides; Design Patterns in Java; Pattern Oriented Software Architecture; UML and the Unified Process, Arlow Neustadt; Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron

Conoscenza approfondita delle metodologie agili di Ingegneria del Software, capacità di sviluppare in maniera critica progetti software complessi.
Applicare tecniche di Machine Learning allo studio di dataset di natura diversa, conoscenza dei principali algoritmi di classificazione e regressione. Capacità di applicazione di tecniche basate su reti neurali e Deep Learning.

Conoscenza di base del linguaggio Python
Conoscenze di base di Ingegneria del Software

Didattica frontale con presentazione di argomenti teorici
Esercitazioni pratiche al calcolatore

Prove Intracorso o equivalente prova scritta, colloquio orale.

No

Modelli di Processo per lo Sviluppo del Software.
Modelli Agili (SCRUM, XP).
Modello Unified Process.
Modello Dev-Ops.
Modello Test Driven Dev.
Requirement Engineering
OOAD - Object Oriented Analysis and Design with UML and UP.
Design Patterns.
Architetture Software.
Service Oriented Architecture:
Architettura SOA
Web Services: WSDL UDDI, SOAP, REST.
Api-gateway e Api-management: OpenAPI (Swagger) vs GraphQL
Microservizi.
Architetture a Microservizi
Tecniche di Deployment di Applicazioni a Microservizi (intro a containers)
Strategie di Migrazione - Soluzione lift & shift
Microservizi per la realizzazione del modello DevOps
Componenti a supporto delle architetture a microservizi: Logging,Transaction tracing, Monitoring (ELK Stack: Elastisearch, Logstash,Kibana)"
Business Processes.
Analisi del Software e Reverse Engineering.
Machine Learning per la Software Engineering.

Tipologie di Learning: Supervised, Unsupervised; Batch, Incrementale; Reinforcement Learning.
Valutazione delle Prestazioni: Funzioni Obiettivo; Parametri ed IperParametri; Tuning; KPIs; Training, Validation e Test; Convergenza, Generalizzazione ed Overfitting.
Classificazione: Classificatore Bayesiano, Support Vector Machines, K-Nearest Neighbor, Decision Tree.
Tecniche di Ensemble: Random Forest
Regressione lineare, polinomiale, Lasso e Ridge.
Clustering: K-means, clustering gerarchico.
Tecniche di Riduzione della Dimensionalità: Principal Component Analysis, Linear Discriminant Analysis
Neural Networks: Hopfield, Multilayers con Backpropagation; Deep Learning: Reti Convolutive e Ricorrenti, le reti LSTM.
Word Embedding.

Italian

In-depth study on Software Engineering topics, in particular Agile methodologies. Microservices development methodologies and use of containers.
Introduction to Machine Learning with insights into the main Classification and Regression techniques. Neural networks and notes on Deep Learning

Design Patterns, Gamma, Helm, Johnson, Vlissides; Design Patterns in Java; Pattern Oriented Software Architecture; UML and the Unified Process, Arlow Neustadt; Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron

In-depth knowledge of agile Software Engineering methodologies, ability to critically develop complex software projects.
Apply Machine Learning techniques to the study of datasets of different nature, knowledge of the main classification and regression algorithms. Ability to apply techniques based on neural networks and Deep Learning.

Basic knowledge of the Python language
Basic knowledge of Software Engineering

Frontal teaching with presentation of theoretical topics
Practical computer exercises

Written and oral exam.

No

Process Models for Software Development.
Agile Models (SCRUM, XP).
Unified Process Model.
Dev-Ops model.
Test Driven Dev Model
Requirement Engineering
OOAD - Object Oriented Analysis and Design with UML and UP.
Design Patterns.
Software Architectures.
Service Oriented Architecture:
SOA architecture
Web Services: WSDL UDDI, SOAP, REST.
Api-gateway and Api-management: OpenAPI (Swagger) vs GraphQL
Microservices.
Microservices architectures
Application Deployment Techniques for Microservices (intro to containers)
Migration Strategies - Lift & shift solution
Microservices for the creation of the DevOps model
Components to support microservice architectures: Logging, Transaction tracing, Monitoring (ELK Stack: Elastisearch, Logstash, Kibana)"
Business Processes.
Software Analysis and Reverse Engineering.
Machine Learning for Software Engineering.

Types of Learning: Supervised, Unsupervised; Batch, Incremental; Reinforcement Learning.
Performance Evaluation: Objective Functions; Parameters and HyperParameters; Tuning; KPIs; Training, Validation and Test; Convergence, Generalization and Overfitting.
Classification: Bayesian Classifier, Support Vector Machines, K-Nearest Neighbor, Decision Tree.
Ensemble Techniques: Random Forest
Linear, polynomial, Lasso and Ridge regression.
Clustering: K-means, hierarchical clustering.
Dimensionality Reduction Techniques: Principal Component Analysis, Linear Discriminant Analysis
Neural Networks: Hopfield, Multilayers with Backpropagation; Deep Learning: Convolutional and Recurrent Networks, LSTM networks.
Word Embedding.