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Machine Learning


Alippi C.

Course director


The course will address the following topics: Supervised learning: linear and nonlinear models for regression and prediction, statistical theory of learning, feature extraction and model selection. Deep learning: architectures including autoencoders, convolutional neural networks and learning procedures. Model performance assessment: cross validation, k-fold cross validation, leave-one-out, bootstrap. Unsupervised learning: K-means clustering, fuzzy C-means, principal component analysis.


Students will learn how to design linear and nonlinear models for regression, prediction and classification as well as assess their performance in a sound way. At the same time, students will learn how to use deep learning architectures and learning algorithms in key real-world applications.

Teaching mode

In presence

Learning methods

Lab sessions will focus on practical aspects and show how to design an appropriate machine learning solution to real-world problems. Basics in Calculus, Probability and Statics are requested

Examination information

Assignments and exam.