Università Roma Tre

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

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