AIMS AND CONTENT

TEACHING METHODS

Combination of lectures, exercises, guided lab activities (MATLAB)

SYLLABUS/CONTENT

•    Data Analysis and Data visualization (wk 1): Types of data. Analysis as modeling. Statistical data analysis. The do’s and don’t’s of data visualization
•    Probability density estimates (wk 2-4): Unsupervised learning. Gaussian model. Principal Component Analysis, Factor Analysis, Independent Component Analysis, Cluster analysis and the EM algorithm.
•    Pattern analysis and decision theory; classifiers (wk 5-6): Bayesian decision theory. Bayes classifiers. Logistic classifiers. Performance of a classifier: ROC curve.
•    Multilayer neural networks (wk 7): Generalized linear models. Perceptrons. Multilayer neural networks and the backpropagation algorithm. Design of a neural network model
•    Graphical Models (wk 8-10) Static data. A general framework for data modeling. The EM algorithm. Regression, decision theory, factor analysis as graphical models. Dynamic graphical models (overview): Temporal data (signals). Discrete vs continuous signals. Hidden Markov Models, Linear dynamical systems as dynamic graphical models
•    Generalization (wk 11-12) : Regularization theory, Vector Quantization. Support Vector Machines.

TEACHERS AND EXAM BOARD

Exam Board

MAURO GIACOMINI (President)

VITTORIO SANGUINETI (President)

MARCO MASSIMO FATO

LESSONS

Class schedule

ANALYSIS OF BIOMEDICAL DATA AND SIGNALS

EXAMS

ASSESSMENT METHODS

•    Written examination (weight 50%)
•    Project work (individuals or couples, weight 50%): Solution of a real problem of biomedical data analysis/processing, chosen from a list of proposed projects; Development of software for calculation/analysis/processing; Final report presenting the results, structured as a scientific article

Exam schedule