ING-INF/05 - 9 CFU - 2° Semester

Teaching Staff


Learning Objectives

Note: This course is offered in English

The course provides an integrated and modern approach to the design and development of intelligent systems, by resorting to state of the art technologies and methods from the fields of machine learning, large-scale multimedia analysis, knowledge representation, natural computation, logic and automated reasoning to solve typical and topical problems in application scenarios such as: human-computer interaction, business intelligence and decision-making support. The students will learn to design, develop and validate systems that learn from heterogeneous data (either in a supervised or unsupervised manner) and are able to detect and recognize patterns; 2) they will learn to program the behaviours of autonomous agents (e.g., NAO robots or similar platforms) capable to interact adaptively with humans. The course provides the theoretical foundations of artificial cognitive systems, but it is essentially practical and application-oriented. The students will gather hands-on experience on frameworks and libraries such as PYTORCH for deep learning; on libraries for multimedia signal processing and data mining; on languages supporting the development of semantic web and logic programming applications.

Course Structure

The course involves frontal lessons, laboratories, and seminars. Attendance is strongly recommended. Attending and actively participating in the classroom activities will contribute positively towards the overall assessment of the oral exam.

Should teaching be carried out in mixed mode or remotely, it might be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Detailed Course Content

Part 1: Knowledge Representation, Reasoning, and Semantic Technologies

Part 2: Machine learning and knowledge discovery from large scale multimedia data

Part 3: Autonomous agents and the NAO humanoid robotic platform

Textbook Information

Selected chapters from the following:

  1. Artificial Cognitive Systems: A Primer. David Vernon, MIT Press, 2014
  2. Artificial intelligence: a modern approach. Stuart Russell, Peter Norvig, 3rd edition, 2010
  3. Data Mining: The Textbook, Charu Aggarwal, 2015. Springer
  4. Deep Learning. I. Goodfellow, Y. Bengio and A. Courville, MIT Press, 2016
  5. A semantic Web Primer (third edition). Grigoris Antoniou, Paul Groth, Frank van Harmelen, and Rinke Hoekstra, 2012. The MIT Press, Cambrigde, Massachusetts, London, England.
  6. Teaching materials provided by the instructor

Open in PDF format Versione in italiano