Electrical Engineering

Introduction to Machine Learning. Basic concepts on supervised and unsupervised learning. Overview of machine learning and deep learning applications. Linear regression models. Least-squares estimation. Maximum Likelihood method. Shrinkage methods (ridge regression, LASSO, elastic net). Classification methods. Linear classifiers, logistic regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, nonparametric methods. Model assessment and selection. Bias-variance decomposition. Generalization capability and generalization error. Overfitting and underfitting. Cross-validation and K-folding. Ockham's razor and early stopping. Nonlinear regression models. Polynomial models, kernel expansion, Bayesian models, neural networks, nonparametric methods. Unsupervised learning. Clustering algorithms and cluster validity methods. Fundamentals of time series prediction. Shallow neural networks. Radial Basis Function (RBF), Fuzzy Inference System (FIS) and ANFIS neurofuzzy networks, Extreme Learning Machine (ELM) and Random Vector Functional-Link (RVFL), Echo State Network (ESN). Basic concepts on randomization. Deep neural networks. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Graph Neural Network (GNN). Fundamentals of hyperdimensional computing. Fundamentals of quantum computing. Quantum gates and quantum gate arrays. Quantum algorithms for optimization and information processing (QFFT, Grover, Schor). Quantum machine learning. Quantum neural networks. Hands-on practices using Matlab and Python: linear regression, overfitting and underfitting; classification and clustering; deep learning; graph neural networks; energy time series prediction; quantum computing and quantum machine learning. Applications and case studies: prediction of renewable energy sources, intelligent energy systems, smart grids; applications to real-world data (logistic, economic, biomedical, mechatronic, environmental, aerospace, etc.); behavioral analysis and biometrics; analysis of materials and industrial processes; machine learning for the IoT/IoE, cooperative and competitive multi-agent learning, smart sensor networks; federated and distributed learning systems.

Fundamentals of Mathematics.

I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press. Teaching material provided through the course's web site.

Lectures with theoretical lessons in the classroom and exercises in the laboratory. During periods when the teaching activity is suspended (due for example to causes of force majeure), remote office hour and e-learning methods replacing lectures will be activated through telematic methods, which will be promptly communicated to the students.

Compulsory attendance is not required.

Oral questions on the topics of the course.

T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning (2nd Ed.), Springer Series in Statistics E. Alpaydin, Introduction to Machine Learning (3rd Ed.), MIT Press [author's notes] C.M. Bishop, Pattern Recognition and Machine Learning, Springer S. Theodoridis, Machine Learning: A Bayesian and Optimization Perspective, Academic Press S.O. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson S. Theodoridis, K. Koutroumbas, Pattern Recognition (4th Ed.), Academic Press B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall

Lectures with theoretical lessons in the classroom and exercises in the laboratory. During periods when the teaching activity is suspended (due for example to causes of force majeure), remote office hour and e-learning methods replacing lectures will be activated through telematic methods, which will be promptly communicated to the students.

Introduction to Machine Learning. Basic concepts on supervised and unsupervised learning. Overview of machine learning and deep learning applications. Linear regression models. Least-squares estimation. Maximum Likelihood method. Shrinkage methods (ridge regression, LASSO, elastic net). Classification methods. Linear classifiers, logistic regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, nonparametric methods. Model assessment and selection. Bias-variance decomposition. Generalization capability and generalization error. Overfitting and underfitting. Cross-validation and K-folding. Ockham's razor and early stopping. Nonlinear regression models. Polynomial models, kernel expansion, Bayesian models, neural networks, nonparametric methods. Unsupervised learning. Clustering algorithms and cluster validity methods. Fundamentals of time series prediction. Shallow neural networks. Radial Basis Function (RBF), Fuzzy Inference System (FIS) and ANFIS neurofuzzy networks, Extreme Learning Machine (ELM) and Random Vector Functional-Link (RVFL), Echo State Network (ESN). Basic concepts on randomization. Deep neural networks. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Graph Neural Network (GNN). Fundamentals of hyperdimensional computing. Fundamentals of quantum computing. Quantum gates and quantum gate arrays. Quantum algorithms for optimization and information processing (QFFT, Grover, Schor). Quantum machine learning. Quantum neural networks. Hands-on practices using Matlab and Python: linear regression, overfitting and underfitting; classification and clustering; deep learning; graph neural networks; energy time series prediction; quantum computing and quantum machine learning. Applications and case studies: prediction of renewable energy sources, intelligent energy systems, smart grids; applications to real-world data (logistic, economic, biomedical, mechatronic, environmental, aerospace, etc.); behavioral analysis and biometrics; analysis of materials and industrial processes; machine learning for the IoT/IoE, cooperative and competitive multi-agent learning, smart sensor networks; federated and distributed learning systems.

Fundamentals of Mathematics.

I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press. Teaching material provided through the course's web site.

Lectures with theoretical lessons in the classroom and exercises in the laboratory. During periods when the teaching activity is suspended (due for example to causes of force majeure), remote office hour and e-learning methods replacing lectures will be activated through telematic methods, which will be promptly communicated to the students.

Compulsory attendance is not required.

Oral questions on the topics of the course.

T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning (2nd Ed.), Springer Series in Statistics E. Alpaydin, Introduction to Machine Learning (3rd Ed.), MIT Press [author's notes] C.M. Bishop, Pattern Recognition and Machine Learning, Springer S. Theodoridis, Machine Learning: A Bayesian and Optimization Perspective, Academic Press S.O. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson S. Theodoridis, K. Koutroumbas, Pattern Recognition (4th Ed.), Academic Press B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall

Lectures with theoretical lessons in the classroom and exercises in the laboratory. During periods when the teaching activity is suspended (due for example to causes of force majeure), remote office hour and e-learning methods replacing lectures will be activated through telematic methods, which will be promptly communicated to the students.

Introduction to Machine Learning. Basic concepts on supervised and unsupervised learning. Overview of machine learning and deep learning applications. Linear regression models. Least-squares estimation. Maximum Likelihood method. Shrinkage methods (ridge regression, LASSO, elastic net). Classification methods. Linear classifiers, logistic regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, nonparametric methods. Model assessment and selection. Bias-variance decomposition. Generalization capability and generalization error. Overfitting and underfitting. Cross-validation and K-folding. Ockham's razor and early stopping. Nonlinear regression models. Polynomial models, kernel expansion, Bayesian models, neural networks, nonparametric methods. Unsupervised learning. Clustering algorithms and cluster validity methods. Fundamentals of time series prediction. Shallow neural networks. Radial Basis Function (RBF), Fuzzy Inference System (FIS) and ANFIS neurofuzzy networks, Extreme Learning Machine (ELM) and Random Vector Functional-Link (RVFL), Echo State Network (ESN). Basic concepts on randomization. Deep neural networks. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Graph Neural Network (GNN). Fundamentals of hyperdimensional computing. Fundamentals of quantum computing. Quantum gates and quantum gate arrays. Quantum algorithms for optimization and information processing (QFFT, Grover, Schor). Quantum machine learning. Quantum neural networks. Hands-on practices using Matlab and Python: linear regression, overfitting and underfitting; classification and clustering; deep learning; graph neural networks; energy time series prediction; quantum computing and quantum machine learning. Applications and case studies: prediction of renewable energy sources, intelligent energy systems, smart grids; applications to real-world data (logistic, economic, biomedical, mechatronic, environmental, aerospace, etc.); behavioral analysis and biometrics; analysis of materials and industrial processes; machine learning for the IoT/IoE, cooperative and competitive multi-agent learning, smart sensor networks; federated and distributed learning systems.

Fundamentals of Mathematics.

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.

T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning (2nd Ed.), Springer Series in Statistics E. Alpaydin, Introduction to Machine Learning (3rd Ed.), MIT Press [author's notes] C.M. Bishop, Pattern Recognition and Machine Learning, Springer S. Theodoridis, Machine Learning: A Bayesian and Optimization Perspective, Academic Press S.O. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson S. Theodoridis, K. Koutroumbas, Pattern Recognition (4th Ed.), Academic Press B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall

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. Basic concepts on supervised and unsupervised learning. Overview of machine learning and deep learning applications. Linear regression models. Least-squares estimation. Maximum Likelihood method. Shrinkage methods (ridge regression, LASSO, elastic net). Classification methods. Linear classifiers, logistic regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, nonparametric methods. Model assessment and selection. Bias-variance decomposition. Generalization capability and generalization error. Overfitting and underfitting. Cross-validation and K-folding. Ockham's razor and early stopping. Nonlinear regression models. Polynomial models, kernel expansion, Bayesian models, neural networks, nonparametric methods. Unsupervised learning. Clustering algorithms and cluster validity methods. Fundamentals of time series prediction. Shallow neural networks. Radial Basis Function (RBF), Fuzzy Inference System (FIS) and ANFIS neurofuzzy networks, Extreme Learning Machine (ELM) and Random Vector Functional-Link (RVFL), Echo State Network (ESN). Basic concepts on randomization. Deep neural networks. Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Graph Neural Network (GNN). Fundamentals of hyperdimensional computing. Fundamentals of quantum computing. Quantum gates and quantum gate arrays. Quantum algorithms for optimization and information processing (QFFT, Grover, Schor). Quantum machine learning. Quantum neural networks. Hands-on practices using Matlab and Python: linear regression, overfitting and underfitting; classification and clustering; deep learning; graph neural networks; energy time series prediction; quantum computing and quantum machine learning. Applications and case studies: prediction of renewable energy sources, intelligent energy systems, smart grids; applications to real-world data (logistic, economic, biomedical, mechatronic, environmental, aerospace, etc.); behavioral analysis and biometrics; analysis of materials and industrial processes; machine learning for the IoT/IoE, cooperative and competitive multi-agent learning, smart sensor networks; federated and distributed learning systems.

Fundamentals of Mathematics.

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.

T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning (2nd Ed.), Springer Series in Statistics E. Alpaydin, Introduction to Machine Learning (3rd Ed.), MIT Press [author's notes] C.M. Bishop, Pattern Recognition and Machine Learning, Springer S. Theodoridis, Machine Learning: A Bayesian and Optimization Perspective, Academic Press S.O. Haykin, Neural Networks and Learning Machines (3rd Ed.), Pearson S. Theodoridis, K. Koutroumbas, Pattern Recognition (4th Ed.), Academic Press B. Kosko, Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence, Prentice-Hall

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.