Artificial Intelligence FOR Signal Processing

Introduzione al data analytics
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

Conoscenze relativi a variabili e processi aleatori

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

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).

Lezioni in aula, esercitazioni, presentazioni assegnate

Frequenza non obbligatoria ma caldamente consigliata

Discussione orale, presentazione tematica assegnata dal docente