Il corso introduce i principi e i metodi fondamentali utilizzati nell'intelligenza artificiale, inclusi i principali paradigmi di machine learning e deep learning, con un focus specifico sugli approcci volti a rivelare le informazioni rilevanti nascoste nei segnali raccolti in applicazioni del mondo reale, come quelli associati a sensori elettrici e meccanici, audio e parlato, immagini e video, o tracciati biologici e medicali, tra molti altri.
scheda docente
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
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
Bibliografia Di Riferimento
Choudhary, K., DeCost, B., Chen, C. et al. Recent advances and applications of deep learning methods in materials science. npj Comput Mater 8, 59 (2022). Malhotra, R., Singh, P. Recent advances in deep learning models: a systematic literature review. Multimed Tools Appl (2023).Modalità Erogazione
Lezioni in aula, esercitazioni, presentazioni assegnateModalità Frequenza
Frequenza non obbligatoria ma caldamente consigliataModalità Valutazione
Discussione orale, presentazione tematica assegnata dal docente