The course aims at providing PhD Candidates knowledge on basic state of the art and advanced theories/techniques for learning from multisensory signals and data Bayesian models for jointly predicting, processing, filtering and interpreting observed interactions. Such models will be shown to enhance functionalities of embodied smart autonomous systems like cars, radios, drones, robots, buildings by providing them a self-awareness information basis. Networks of self-aware autonomous systems interacting in smart cognitive environment will be the also targeted as examples carried on in the course. From a methodological viewpoint, this module aims at identifying and describing methodologies and techniques for defining a…
Agile development is the ability to develop software quickly, in the face of rapidly changing requirements. By following agile principles and practices, a software development team is encouraged to evolve into a self-organizing and cross-functional group, thus improving the quality of the code, the quality of deliveries and in the end the customer satisfaction. In general, agile concepts promote adaptive planning, evolutionary development and delivery, a time-boxed iterative approach, and encourage rapid and flexible response to change. Design principles described during the course will help to keep the software flexible and maintainable, while design patterns will represent some practical tools…
The course intends to provide students with the basic knowledge and skills for developing microcontroller-based applications. After a short introduction on edge computing and microcontrollers, the course will focus on the programming of STM32F4 boards, as an example of the Cortex M families, considering the main basic features and peripherals, such as: General Purpose Input Output (GPIO), Interrupts, Universal Synchronous/Asynchronous Receiver-Transmitter (USART/UART), Direct Memory Access (DMA), I2C, Wifi connectivity. The course will involve hands-on activities on all the addressed topics.
The course provides advanced skills related to data analysis. It provides insights on data mining methodologies and the applications of these methodologies to knowledge extraction from data. The student will learn both the theoretical background and the practical issues for data analysis. Learning This course aims at providing an introductory and unifying view of information extraction and model building from data, as addressed by many research fields like DataMining, Statistics, Computational Intelligence, Machine Learning, and PatternRecognition. The course will present an overview of the theoretical background of learning from data, including the most used algorithms in the field, as well…
The course aims at providing PhD Candidates knowledge on basic state of the art and advanced theories/techniques for learning from multisensory signals and data Bayesian models for jointly predicting, processing, filtering and interpreting observed interactions. Such models will be shown to enhance the functionalities of embodied smart autonomous systems like cars, radios, drones, robots, buildings. by providing such agents of a self-awareness information layer. Networks of self-aware autonomous systems interacting in smart cognitive environment will be also targeted as examples carried on in the course. From a methodological viewpoint, this module aims at identifying and describing methodologies and techniques for…
Agile development is the ability to develop software quickly, in the face of rapidly changing requirements. By following agile principles and practices, a software development team is encouraged to evolve into a self-organizing and cross-functional group, thus improving the quality of the code, the quality of deliveries and in the end the customer satisfaction. In general, agile concepts promote adaptive planning, evolutionary development and delivery, a time-boxed iterative approach, and encourage rapid and flexible response to change. Design principles described during the course will help to keep the software flexible and maintainable, while design patterns will represent some practical tools…
The course aims at providing PhD Candidates knowledge on basic state of the art and advanced theories/techniques for learning from multisensory signals and data Bayesian models for jointly predicting, processing, filtering and interpreting observed interactions. Such models will be shown to enhance the functionalities of embodied smart autonomous systems like cars, radios, drones, robots, buildings, by providing such agents with a self-awareness information layer. Networks of self-aware autonomous systems interacting in smart cognitive environment will be also targeted as examples carried on in the course. From a methodological viewpoint, this module aims at identifying and describing methodologies and techniques for…