ARTIFICIAL INTELLIGENCE: MACHINE LEARNING AND PATTERN RECOGNITION

Academic year
2017/2018 Syllabus of previous years
Official course title
ARTIFICIAL INTELLIGENCE: MACHINE LEARNING AND PATTERN RECOGNITION
Course code
CM0492 (AF:248720 AR:136295)
Modality
On campus classes
ECTS credits
6
Degree level
Master's Degree Programme (DM270)
Educational sector code
INF/01
Period
2nd Semester
Course year
1
Where
VENEZIA
The course aims at introducing the student to the principles, the algorithms and the main applications of Artificial Intelligence, with a focus on inference, machine learning and pattern recognition.
The student is expected to be familiar with the basic concepts of calculus and probability theory.
Information theory and inference: source coding, channel coding.

Learning and inference in neural networks: feed-forward networks, deep learning architectures, Hopfield networks and related models.

Unsupervised and semi-supervised learning: K-means, spectral clustering, dominant sets, game-theoretic models.
D. J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
oral
Powerpoint presentations and chalk talk.
English
To favor an "active" appraoch to the study of the topics covered in the classes, students will be asked to develop a simple project which will be discussed during the oral examination.