INTELLIGENZA ARTIFICIALE

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, 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 on the NAO autonomous, programmable humanoid robot.

Knowledge of a programming language (any). Good software developments skills are not mandatory, but are a definite asset.

Strongly recommended.

Part 1: Knowledge Representation and Semantic Technologies

• Introduction to Knowledge-Based Systems and to Knowledge Representation
• PROLOG: Facts, Queries and Rules. First order logic and backtracking. Recursion, Lists, and a Simple Planning Problem. Negation-as-failure, Cut and Fail. Grammars and problems on graphs.
• The Semantic Web: The RDF Data Model. OWL: The Web Ontology Language. Ontology Engineering. Examples in Protege. The SPARQL query language. Reasoners. Other Semantic Web Technologies and Applications. Linked data.

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

• Supervised learning: Probabilistic models, linear models, kernel-based methods, Support Vector Machines, data pre-processing
• Neural models: networks, model design, backpropagation training
• Deep learning: issues with "deep" models, training methods, convolutional neural networks (CNN), Recurrent Neural Networks
• Multimedia analysis: Fundamental of multimedia signal processing (audio, speech, video). Image representations, filters, edge and contour extractions, segmentation. Image description; features and feature extraction, SIFT, bag-of-words models. Artificial vision. Object detection and recognition. Towards scene understanding.
• Knowledge discovery from data: the general data mining process, model construction and testing, performance evaluation, big data and scalability issues, applications: recommender systems and business intelligence

Part 3: Autonomous agents and the NAO humanoid robotic platform

• Theories of perception, action and interaction. Interactive autonomous agents. Human-robot interaction
• The Nao robot operating system (NAOqi), the graphical porgramming environment Choreographe, NAO SDK.
• Applications. Augmenting the NAO perceptual and cognitive system.

Selected chapters from the following resources:

1. A semantic Web Primer (third edition). Grigoris Antoniou, Paul Groth, Frank van Harmelen, and Rinke Hoekstra, 2012. The MIT Press, Cambrigde, Massachusetts, London, England.
2. Semantic Web for the Working Ontologist (Second Edition). Dean Allemang and James Hendler, 2011. Elsevier.
3. Machine Learning: A Probabilistic Perspective. Kevin Murphy, MIT Press
4. Computer Vision: A Modern Approach. David A Forsyth, Jean Ponce, 2015. Pearson education Limited
5. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision (2004) 60:91.
6. Data Mining: The Textbook, Charu Aggarwal, 2015. Springer
7. Teaching materials provided by the instructor

All the teaching materials and resources for the course will we available on Studium.

The exam consists of 3 homeworks to be handed in during the course (60% of the final grade), and of a final project to be presented at the end of the course (40% of the final grade).