Syllabus and Academic Calendar

Transcription

1 Erasmus Mundus Joint Doctoral Programme in Syllabus and Academic Calendar 2011 Management Board

2 ICE PhD Courses Code Hours Title Teacher(s) University of Genoa Data Fusion and Bayesian Interaction Modeling for Cognitive Ambient Intelligence Prof. Carlo Regazzoni Theory and Practice of Learning from Data Prof. Davide Anguita Modeling, Simulation and Games Prof. Francesco Bellotti, Prof. Alessandro De Gloria Enabling pervasive communication environments Prof. Raffaele Bolla 2

3 ICE_UNIGE_01: Data Fusion and Bayesian Interaction Modeling for Cognitive Ambient Intelligence Hours: 25 Teacher(s): Carlo Regazzoni Abstract: The course aims at providing PhD students knowledge on basic tools in data fusion domain together with more advanced theories for representing, modelling and automatically interpreting interactions occurring between users, between users and artificial systems etc. within a smart cognitive environment. A Bayesian approach is used as a main methodological track in the course. In particular this module aims at : providing a common framework to identify and to describe methodologies and techniques for integrating multisensorial contextual data by using Data Fusion paradigms and techniques providing a common framework for defining behavioural artificial models for context based, adaptive and personalized decision steps used by cognitive system to address and react with respect to different contextual working situations. Showing examples and applications of specific techniques within cognitive telecommunication systems by means of description of two main case studies: cognitive radio and multisensor/multimodal cognitive human machine interfaces in smart spaces. Program: Data Fusion methodologies and techniques for integrating multisensorial contextual data Data Fusion models: the JDL model and its extensions: signals, objects, situations, threats, processes and cognitive refinement. Alignment, association, state extimations steps in data fusion levels. Alignment techniques: Space, Time, Frequency calibration techniques in video and radio based systems. Multisensor data association techniques: nearest neighbour, PDAF and JPDAF. State estimation techniques: from Kalman filter to non linear and non Gaussian state estimation techniques (extended Kalman Filter, Unscented Kalman Filter, Mean Shift, Particle Filters). Bayesian Networks for scene interpretation. Distributed Data Fusion (DDF): models and techniques. Distributed decision theory. Interaction Modeling. Bio inspired behavioral cognitive artificial models for context based, adaptive and personalized decision. Neural basis of consciousness: the Damasio model (core self, protoself, autobiographical memory and autobiographical self). The brain, memory and prediction. Adaptive and personalized embodied decision models for analyzing situations and driving actions and re actions within cognitive systems. Decision space representation. Autobiographical memories and their representation and estimation through Bayesian learning techniques. Applications and case studies: Cognitive radio: Behavioral models for interactions between base stations 3

4 and mobile terminals. Cognitive safety and physical security systems (smart patrolling in cooperative environments, preventive automotive vehicles, smart buidings, etc.) Bibliography: Course slides will be provided and made available at Further reading: David L. Hall, James Llinas, Handbook of Multisensor Data Fusion, CRC Press, 2001; Y.Bar Shalom, W.D.Blair, Multitarget Multisensor Tracking: Applications and Advances, second edition, Artech House, 2000; Pramod Varshney, Distributed Dtection and Data Fusion, Springer, 1997 Joseph Mitola III, Aware, Adaptive and Cognitive Radio: The Engineering Foundations of Radio XML Wiley Interscience, 2006 Anthonio Damasio, The feeling of what happens: Body and Emotion in the Making of Consciousness (1999), Harcourt Brace & Company, Contact: Assessment: Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through an oral presentation. 4

5 ICE_UNIGE_02: Theory and Practice of Learning from Data Hours: 20 Teacher(s): Davide Anguita Abstract: This course aims at unifying the different views of model building from experimental data, as addressed by many research fields like Computational Intelligence, Pattern Recognition, Data Mining, Machine Learning and Statistics. The problem of building a model for understanding a physical phenomenon is traditionally approached by creating a reasonable mathematical representation and subsequently tuning and validating it through experimental data. Then, the obtained model can be used to better understand and make effective predictions about the events under observation. A more modern approach, instead, which has been named the learning approach, tries to automate this procedure, starting from the available data and building the optimal model, according to some quality measures, limiting or avoiding any user intervention. The course will present the theoretical background of learning from data as well as practical applications in several areas including industrial (e.g. automotive, robotics, etc.), scientific (e.g. cognitive science, medical and bioinformatics) and economic (e.g. time series prediction, market analysis) fields. Program: Data, information and models: induction, deduction and transduction Statistical inference: Bayesians vs. Frequentists Simple models: Associations rules, Classification trees, Naïve Bayes Classification and Regression Nonlinear models: Neural Networks and Kernel methods Model selection and error estimation Clustering, Novelty Detection and Ranking Bibliography: Course notes will be provided and made available at Further readings: V.Cherkassky, F.Mulier, Learning from Data: Concepts, Theory and Methods, T.Hastie, R.Tibshirani, J.Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Contact: Davide.Anguita@unige.it Assessment: Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through an oral presentation. 5

6 ICE_UNIGE_03: Modelling, simulations and games Hours: 20 Credits: 5 Teacher(s): Francesco Bellotti, Alessandro De Gloria Abstract: The course aims at providing PhD students with knowledge on principles, design theories and development tools for modelling and simulation. Applications in the Serious Gaming field is a major focus of the course. Program: Modeling and simulation 3D modeling theory and tools 3D rendering theory and tools Animation High performance parallel computing The High Level Architecture (HLA) standard for distributed simulation and interoperability of simulations Non Player characters (movement, behavior, knowledge) Serious Game Applications State of the art of Serious Games for educational domains such as: safety, crisis management, cultural heritage o Targets, achievements and research perspectives Requirements from target users and stakeholders o Gathering requirements and defining specifications Pedagogical and psychological foundations Architectures o Game engines and additional Artificial Intelligence modules o Programming languages o Interoperability and semantics Contents o Authoring tools Deployment and testing o Integration of SGs in educational contexts and in corporate training o Qualitative and quantitative methods for impact assessment Bibliography: Course notes will be provided and made available at Further reading: Buckland, Programming Game AI by Example, Wordware Akenine Moeller, Haines, Hoffman, Real Time Rendering, A K Peters Zeigler, Praehofer, Kim, Theory of Modeling and Simulation, Academic Press Nitschke, Professional XNA Game Programming: For Xbox 360 and Windows, John Wiley & Sons 6

7 Prensky, Teaching Digital Natives Partnering for Real Learning, Corwin Prensky, Digital Game Based Learning, McGraw Hill Contact: Assessment: Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through an oral presentation. 7

8 ICE_UNIGE_04: Dynamic Networking in Pervasive Environments Hours: 20 Credits: 5 Teacher(s): Raffaele Bolla Abstract: This course will introduce networking key aspects for building pervasive communication environments. The general architecture of these systems includes location awareness, resource discovery and control, context awareness, security and, obviously, mobility management. Program: Pervasive communication and ubiquitous computing Ubiquitous computing: concept and challenges Ubiquitous network access: pervasive communication Network architecture for pervasive media access Resource discovery Context awareness and sensor networks Mobility Taxonomy of mobility in data networks Terminal mobility o Handover: link layer, network layer, transport layer, application layer o Network mobility Session migration User centric pervasive communication User centric media User centric networking Bibliography: Course notes will be provided and made available at Further reading: Y. B. Lin and A. C. Pang, Wireless and Mobile All IP Networks. Wiley, November Charles E. Perkins, Mobile IP Design Principles And Practices. Pearson Education, M. Weiser, Some computer science issues in ubiquitous computing, Communications of the ACM, vol. 36, no. 7, pp , S. Helal, Standards for service discovery and delivery, IEEE Pervasive Computing, vol. 1, no. 3, pp , July September H. Schulzrinne, X. Wu, S. Sidiroglou, and S. Berger, Ubiquitous computing in home networks, IEEE Communications Magazine, vol. 41, no. 11, pp , November Contact: raffaele.bolla@unige.it Assessment: Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through an oral presentation. 8

9 ICE PhD Courses General Rules This note contains some general rules to ensure that all the courses offered in the ICE PhD program follow highquality standards and present to the students a comprehensive and harmonized view of the ICE PhD courses. 1. Language Both the lectures and the teaching material are in English. 2. Didactical Material Each course provides the didactical material (slides, lecture notes, software, etc.) in advance through the ICE web site and/or through the Didactic Manager. 3. Assessment Each course assesses the students through a final test, shortly after the completion of the lectures. The assessment is carried out by the same people who taught the course. The assessment consists in one of the following: Written exam: multiple choice test, free response test or exercises; Position paper: the candidate should write a short position paper on how the topics dealt with during the course can be of interest for his/her research and defend it through a oral presentation. The final outcome of the test is passed / failed. 9

10 EMJD ICE: University of Genoa Courses Courses and features Course 1 Course 2 Course 3 Course 4 Course Data Fusion and Bayesian Interaction Modeling for Cognitive Ambient Intelligence Theory and Practice of Learning from Data Enabling pervasive communication environments Modeling, Simulation and Games Professor(s) Prof. Carlo Regazzoni Prof. Davide Anguita Prof. Raffaele Bolla Contact carlo@dibe.unige.it Davide.Anguita@unige.it Raffaele.Bolla@unige.it Prof. Francesco Bellotti,Prof. Alessandro De Gloria franz@elios.unige.it, Alessandro.DeGloria@unige.it Starting Date Mon 13 June 2011 Mon 20 June 2011 Mon 27 June 2011 Mon 18 July 2011 Hours of Lesson Weekly Schedule Daily, Mon to Fri, 09:30 13:30 Daily, Mon to Fri, 09:30 13:30 Daily, Mon to Fri, 09:00 13:00 Daily, Mon to Fri, 09:30 13:30 Room, Building D2, Ex CNR Building, Via Opera Pia 11A D2, Ex CNR Building, Via Opera Pia 11A D2, Ex CNR Building, Via Opera Pia 11A D2, Ex CNR Building, Via Opera Pia 11A Notes Academic Year