The medium of instruction is English. The course introduces statistical and machine learning methods for processing data and signals in order to extract information from them. Such methods are based on models that are typically learned from training data. In particular, the course covers statistical methods, where the data are modelled as a set of measured (or acquired) samples of a random process, and neural networks, where the model is a suitable combination of basic units (neurons) and the model parameters are found through numerical optimization. The first part of the course deals with statistical learning in maximum likelihood and Bayesian sense. The techniques presented during the course are applied to classification problems in several domains of ICT, including classification of continuous as well as discrete data, and dimensionality reduction using principal component analysis and kernel methods. In the second part of the course, feedforward and convolutional neural networks are introduced. Finally, statistical tracking filters are introduced. Throughout the course, the concepts above are illustrated with practical examples taken from ICT applications. Half of the course takes place in the LAIB laboratories, where students implement and assess the methods discussed during the lectures. Most of the activities are performed using Matlab and Python, whereas the TensorFlow framework (also based on Python) is employed for neural networks experiments. This course is designed jointly with the course “ICT for health”, with the objective to provide students with a coordinated “machine learning” approach that can be applied to several ICT problems; in particular, this course deals primarily with machine learning basics, classification and neural networks, while the “ICT for health” course addresses regression and clustering topics.

1. Knowledge of the basics of statistical learning. 2. Knowledge of the theory and methods of classification for continuous and discrete data using maximum likelihood and Bayesian methods. 3. Knowledge of dimensionality reduction techniques and kernel methods. 4. Knowledge of neural networks: training/testing and applications. 5. Knowledge of tracking filters. 6. Ability to use Matlab and Python programming frameworks, as well as the TensorFlow environment.

The student must know the following concepts: 1. Random variables/processes and probability density function, mean and variance 2. Linear algebra 3. Programming in the Matlab language At the beginning of the course, random variables and multivariate distributions are briefly reviewed.

Discrete-time random signals and processes, probability distributions, model fitting (1.2CFU), including: • Introduction to machine learning; supervised vs. unsupervised learning, regression vs. classification, k-NN classifier, the curse of dimensionality, overfitting. • Review of probability, continuous vs. discrete random variables, Bayes’ rule, independence, common probability distributions, Gaussian multivariate distribution. Foundations of estimation theory (1.2CFU), including: • Maximum likelihood estimation • Bayesian and MAP estimation • Application to Gaussian processes Classification and latent linear models (1.2CFU), including: • Hypothesis testing • Bayesian decision theory and Bayesian classifiers • Performance assessment of a classifiers • Principal component analysis • Kernel methods and support vector machines Feedforward and convolutional deep neural networks (1.8CFU), including: • Perceptrons and architecture of a neural network • Backpropagation algorithm • Loss functions, overfitting and regularization • Convolutional networks and deep learning Tracking filters (0.6CFU), including • Kalman filter

Half of the course takes place in the LAIB laboratories, where students implement and assess the methods discussed during the lectures. Most of the activities are performed using Matlab, whereas Python and TensorFlow are employed for neural networks experiments.

The following texts will be used during the course: Regarding statistical models and machine learning, the reference book is: K.P. Murphy, “Machine learning – a probabilistic perspective”, MIT press, 2012. Regarding neural networks, the reference book is the online book “Neural networks and deep learning” (2017), available at link: http://neuralnetworksanddeeplearning.com

Modalità di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;

Modalità di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;