ARTIFICIAL INTELLIGENCE: MACHINE LEARNING AND PATTERN RECOGNITION

Academic year 2019/2020 Syllabus of previous years
Official course title ARTIFICIAL INTELLIGENCE: MACHINE LEARNING AND PATTERN RECOGNITION
Course code CM0472 (AF:306568 AR:166135)
Modality On campus classes
ECTS credits 6 out of 12 of ARTIFICIAL INTELLIGENCE
Degree level Master's Degree Programme (DM270)
Educational sector code INF/01
Period 2nd Semester
Course year 1
Where VENEZIA
Contribution of the course to the overall degree programme goals
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.
Expected learning outcomes
1. Knowledge and understanding
1.1. acquire the main models and algorithms of machine learning and pattern recognition

2. Ability to apply knowledge and understanding
2.1. acquire the ability to apply the studied models to real problems;
2.2. acquire the ability to critically assess the performance and the behavior of a model applied to a concrete problem

3. Judgement
3.1. ability to understand which characteristics of the various models of artificial intelligence are best suited to a given problem;
3.2. ability to critically evaluate the theoretical characteristics of the proposed models;
Pre-requirements
The student is expected to be familiar with the basic concepts of calculus and probability theory.
Contents
Information theory and inference: source coding, channel coding.

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

Unsupervised and semi-supervised learning: K-means, spectral clustering, dominant sets, game-theoretic models.
Referral texts
D. J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
Assessment methods
Oral exam and discussion of a project agreed with the teacher.
Teaching methods
Powerpoint presentations and chalk talk.
Teaching language
English
Further information
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.
Type of exam
oral
Definitive programme.
Last update of the programme
15/07/2019