The course aims to introduce the problems related to the implementation of machine learning applications and algorithms on platforms other than high-performance servers available in the cloud.

The contents and the thus the skills acquired at the end of the course include both the hardware aspects of the problem (architectures of "edge" devices) and the software aspects (programming models, protocols and related APIs). The skills acquired will allow a correct understanding of decentralized systems in which the flow of data is processed not only on servers, but rather locally on devices with reduced computational resources and energy.

- Theory and basic concepts of machine-learning - Software programming theories and tools - Object-oriented programming - Basic concepts on computer networks and architectures

● Hardware [30h]: o Computer architectures used for running and developing "edge" machine learning algorithms: ▪ "quantitative" analysis between single/multi-core microprocessors, microcontrollers, DSP, PLC, GPU, etc. ▪ implications on the programming model o Basic knowledge about sensors: filtering, conversion o understanding, modeling and optimization of non-functional metrics (energy, performance) ● Software [24h]: o Classification and taxonomy of ML [4h]: ▪ real problems in IT-industrial applications ▪ machine-learning algorithms o Distributed software for edge computing [20h] ▪ Event processing / sensor fusion ▪ Management of edge-fog-cloud interfaces (web programming/network programming of IoT protocols - REST response and Publish subscribe) ▪ Microservices design patterns ▪ Cloud/edge workload balancing

- First Part [8h]: board embedded (e.g., Raspberry, Zynq), configuration of several setting parameters and their exploration, assessment of different FoMs - Second Part [8h]: Management of edge-fog-cloud interface (web/net programming using IoT protocols - REST/ MQTT) - Third Part [8h]: interface with cloud and web services

Class handouts and additional material will be made available on the course webpage. User guides for lab sessions will be made available as well.

Modalità di esame: Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo; Elaborato progettuale in gruppo;

The exam includes two main mandatory parts: 1) written test on the theoretical aspects introduced during the course (multiple choice and open-ended questions using the online platform Exam integrated with proctoring tools Respondous); 2) the evaluation of the report on a team project assigned at the end of the course.

Modalità di esame: Test informatizzato in laboratorio; Prova scritta tramite PC con l'utilizzo della piattaforma di ateneo; Elaborato progettuale in gruppo;

The exam includes two main mandatory parts: 1) written test on the theoretical aspects introduced during the course (multiple choice and open-ended questions using the online platform Exam integrated with proctoring tools Respondous); 2) the evaluation of the report on a team project assigned at the end of the course.