Master's degree in Computer Science and Engineering

Master's degree in Computer Science and Engineering

Fundamentals of Machine Learning (2020/2021)

Course code
Marco Cristani
Academic sector
Language of instruction


Teaching is organised as follows:
Activity Credits Period Academic staff
Teoria 5 II semestre Marco Cristani
Laboratorio 1 II semestre Marco Cristani

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Learning outcomes

The course aims to provide the theoretical foundations and describe the main methodologies relating to the machine learning area. In particular, the course will deal with the methods of analysis, recognition and automatic classification of data of any type, typically called patterns. These disciplines are the basis, are used, and often complete many other disciplines and application areas of wide diffusion, such as computational vision, robotics, image processing, data mining, analysis and interpretation of medical and biological data, bioinformatics, biometrics, video surveillance, speech and text recognition and many others. More precisely, the methodologies that will be introduced in the course are often an integral part of the aforementioned application areas, and constitute the "intelligent" part with the final objective of understanding (classifying, recognizing, analyzing) the data coming from the process of interest ( whether they are signals, images, strings, categorical, or other types). Starting from the type of measured data, the entire analysis pipeline will be considered such as the extraction and selection of characteristics; supervised and unsupervised machine learning methods, parametric and non-parametric analysis techniques, and validation protocols. Finally, the recent deep learning techniques will be analyzed in general with some case studies. In conclusion, the course aims to provide the student with a set of theoretical foundations and algorithmic tools to address the problems that can be encountered in strategic and innovative industrial sectors such as those involving the processing of large amounts of data (big data), multimedia, visual inspection of products and automation in general.


The course can be divided into two parts, the methodology and the application, which go hand in hand during the course.

- Introduction
- Recognition and classification
- Bayesian Decision Theory
- Parameters Estimation
- Nonparametric Methods of Parameters Estimation
- Linear and non-linear discriminant functions
- Extraction and feature selection, PCA, Fisher transform
- Expectation-Maximization Algorithm on mixtures of Gaussians
- Generative and discriminative methods
- Kernel Methods and Support Vector Machines
- Hidden Markov Models
- Methods for unsupervised classification (clustering)
- Pattern recognition for the analysis and recognition in images and videos

- Face recognition
- Tracking
- Video surveillance

- Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification (2nd Edition). Wiley-Interscience.
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.

Assessment methods and criteria

oral exam

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