COGNITIVE COMPUTING AND ARTIFICIAL INTELLIGENCE

ING-INF/05 - 9 CFU - 2° Semester

Teaching Staff

DANIELA GIORDANO


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 art technologies and methods from the fields of machine learning, knowledge representation, natural computation, logic and automated reasoning to solve typical and topical problems in application scenarios such as: business intelligence, decision-making support, human-computer interaction. 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 languages supporting the development of semantic web and logic programming applications.

Learning objectives:

Knowledge and understanding

Applying knowledge and understanding

Making judgements

Communication skills

Learning skills


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