Telecommunication Engineering

Introduction to Machine Learning. Artificial intelligence, machine learning and neural networks. Basic concepts of supervised and unsupervised learning. Overview of applications of machine learning and deep learning. Applications of machine learning to electrical and electronic systems. Practical approaches to the use of neural networks. Perceptrons. Data-driven learning, gradient descent and backpropagation. Model validation, overfitting and underfitting. Regularization. Data preparation. Introduction to time series prediction. Shallow neural networks. Radial Basis Function (RBF), Fuzzy Inference System (FIS) and neurofuzzy networks ANFIS, Extreme Learning Machine (ELM) and Random Vector Functional-Link (RVFL), Echo State Network (ESN). Basic concepts of randomization. Deep neural networks. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN). Computational aspects and hardware implementation of neural networks. Introduction to hyperdimensional computing. Circuit architectures for machine learning. Vector processor design methods. Dependence Graph (DG) and dependency analysis. Projection and scheduling, canonical mapping from DG to SFG, examples. Systolic arrays, fused multiply-add (FMA), pipelining, systolization. Parallel calculation on multi-core architectures, FPGA, GPU, TPU. Machine learning applied to computational architectures, circuits and systems. Main concepts, case studies, problems, solutions and future developments. Machine learning applied to electrical and environmental systems. Intelligent sensors, fault diagnosis, battery design and control systems. Machine learning applied to energy networks. Prediction from renewable sources, intelligent energy infrastructures, smart grids. Introduction to quantum computing. Quantum gates and arrays. Quantum algorithms for optimization and information processing (QFFT, Grover, Schor). Quantum machine learning. Quantum neural networks. Overview of the implementation on optical and optoelectronic devices. Practical exercises (hands-on) using Matlab: deep learning; GPU and parallel computing; prediction of energy time series; quantum computing and quantum machine learning. Applications and case studies: energy prediction from renewable sources, intelligent energy systems, Smart Grids; applications to real data (logistics, economical, biomedical, mechatronics, environmental and aerospace, etc.); biometrics and behavioral analysis; implementation of neural and neurofuzzy networks on microcontrollers, embedded systems and smart sensors.

Basic knowledge of electronic and signal processing systems.

H.T. Kung, R. Sproull, G. Steele, VLSI Systems and Computations, Springer. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press. Teaching material provided through the course's web site.

Compulsory attendance is not required.

Oral questions on the topics of the course. Didactic/experimental homeworks on a course topic.

H.T. Kung, R. Sproull, G. Steele, VLSI Systems and Computations, Springer. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press S.O. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson C.M. Bishop, Pattern Recognition and Machine Learning, Springer T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning (2nd Ed.), Springer Series in Statistics

Teaching in presence. In cases of force majeure (health emergencies, etc.) the remote and/or blended methods will also be activated as per current rules.

Introduction to Machine Learning. Artificial intelligence, machine learning and neural networks. Basic concepts of supervised and unsupervised learning. Overview of applications of machine learning and deep learning. Applications of machine learning to electrical and electronic systems. Practical approaches to the use of neural networks. Perceptrons. Data-driven learning, gradient descent and backpropagation. Model validation, overfitting and underfitting. Regularization. Data preparation. Introduction to time series prediction. Shallow neural networks. Radial Basis Function (RBF), Fuzzy Inference System (FIS) and neurofuzzy networks ANFIS, Extreme Learning Machine (ELM) and Random Vector Functional-Link (RVFL), Echo State Network (ESN). Basic concepts of randomization. Deep neural networks. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN). Computational aspects and hardware implementation of neural networks. Introduction to hyperdimensional computing. Circuit architectures for machine learning. Vector processor design methods. Dependence Graph (DG) and dependency analysis. Projection and scheduling, canonical mapping from DG to SFG, examples. Systolic arrays, fused multiply-add (FMA), pipelining, systolization. Parallel calculation on multi-core architectures, FPGA, GPU, TPU. Machine learning applied to computational architectures, circuits and systems. Main concepts, case studies, problems, solutions and future developments. Machine learning applied to electrical and environmental systems. Intelligent sensors, fault diagnosis, battery design and control systems. Machine learning applied to energy networks. Prediction from renewable sources, intelligent energy infrastructures, smart grids. Introduction to quantum computing. Quantum gates and arrays. Quantum algorithms for optimization and information processing (QFFT, Grover, Schor). Quantum machine learning. Quantum neural networks. Overview of the implementation on optical and optoelectronic devices. Practical exercises (hands-on) using Matlab: deep learning; GPU and parallel computing; prediction of energy time series; quantum computing and quantum machine learning. Applications and case studies: energy prediction from renewable sources, intelligent energy systems, Smart Grids; applications to real data (logistics, economical, biomedical, mechatronics, environmental and aerospace, etc.); biometrics and behavioral analysis; implementation of neural and neurofuzzy networks on microcontrollers, embedded systems and smart sensors.

Basic knowledge of electronic and signal processing systems.

H.T. Kung, R. Sproull, G. Steele, VLSI Systems and Computations, Springer. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press. Teaching material provided through the course's web site.

Compulsory attendance is not required.

Oral questions on the topics of the course. Didactic/experimental homeworks on a course topic.

H.T. Kung, R. Sproull, G. Steele, VLSI Systems and Computations, Springer. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press S.O. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson C.M. Bishop, Pattern Recognition and Machine Learning, Springer T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning (2nd Ed.), Springer Series in Statistics

Teaching in presence. In cases of force majeure (health emergencies, etc.) the remote and/or blended methods will also be activated as per current rules.