The course introduces the fundamental principles and methods used in artificial intelligence, including the main machine learning and deep learning paradigms, with a specific focus on the approaches aiming to unveil the relevant information hidden in signals collected in real-world applications, such as those associated with electrical and mechanical sensors, audio and speech, images and videos, or biological and medical records, among many others.
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materiale didattico
Statistics
inference and statistical hypothesis testing
regression
Machine Learning
classification (supervised learning)
decision trees, random forests, naïve Bayes, linear discriminant analysis, k-nearest neighbor, support vector machines
clustering (unsupervised learning)
k-means clustering
hierarchical clustering
data modeling
principal component analysis, indipendent component analysis, outlier detection and data cleansing, hidden Markov models
deep learning & CNN
Processing
examples in Matlab & Python
Students' presentations
G. James, D. Witten, T. Hastie, R. Tibshirani, "An Introduction to Statistical Learning"
K. P. Murphy, "Machine Learning - A Probabilistic Perspective"
S. Theodoridis and K. Koutroumbas, "Pattern Recognition"
T. A. Runkler, "Data Analytics - Models and Algorithms for Intelligent Data Analysis"
I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning"
Materiale didattico ulteriore fornito dal docente
Mutuazione: 20810547 ARTIFICIAL INTELLIGENCE FOR SIGNAL PROCESSING in Ingegneria delle Telecomunicazioni LM-27 MAIORANA EMANUELE
Programma
Introduzione al data analyticsStatistics
inference and statistical hypothesis testing
regression
Machine Learning
classification (supervised learning)
decision trees, random forests, naïve Bayes, linear discriminant analysis, k-nearest neighbor, support vector machines
clustering (unsupervised learning)
k-means clustering
hierarchical clustering
data modeling
principal component analysis, indipendent component analysis, outlier detection and data cleansing, hidden Markov models
deep learning & CNN
Processing
examples in Matlab & Python
Students' presentations
Testi Adottati
S. Nolan and T. Heinzen, "Statistics for the Behavioral Sciences"G. James, D. Witten, T. Hastie, R. Tibshirani, "An Introduction to Statistical Learning"
K. P. Murphy, "Machine Learning - A Probabilistic Perspective"
S. Theodoridis and K. Koutroumbas, "Pattern Recognition"
T. A. Runkler, "Data Analytics - Models and Algorithms for Intelligent Data Analysis"
I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning"
Materiale didattico ulteriore fornito dal docente
Modalità Erogazione
Lezioni in aula, esercitazioni, presentazioni assegnateModalità Frequenza
Frequenza non obbligatoria ma caldamente consigliataModalità Valutazione
Discussione orale, presentazione tematica assegnata dal docente