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, natural computation, knowledge representation, logic and automated reasoning to solve typical and topical problems in application scenarios such as: natural 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 (NAO robots) 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 languages supporting the development of semantic web and logic programming applications, on frameworks and libraries such as TORCH for deep learning; on MATLAB libraries for multimedia signal processing and data mining; and with the NAO autonomous, programmable humanoid robot.

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.

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. Computer Vision: A Modern Approach. David A Forsyth, Jean Ponce, 2015. Pearson education Limited
  7. Teaching materials provided by the instructor

Open in PDF format Versione in italiano