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.
teacher profile teaching materials
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"
Further material provided by the teacher
Mutuazione: 20810547 ARTIFICIAL INTELLIGENCE FOR SIGNAL PROCESSING in Ingegneria delle Telecomunicazioni LM-27 MAIORANA EMANUELE
Programme
Introduction to 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
Core Documentation
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"
Further material provided by the teacher
Type of delivery of the course
Lectures, exercises, presentationsAttendance
Attending lectures is not mandatory yet strongly encouragedType of evaluation
Oral discussion, presentation on a topic selected by the teacher