PhD Program in Bioengineering and Robotics - 2014 Available PhD Courses for 2014 Artificial Cognitive Systems .............................................................................................................................. 3 Regularization Methods for High Dimensional Machine Learning ................................................................. 5 Introductory design of mechatronic systems .................................................................................................. 6 Advanced EEG analyses .................................................................................................................................... 8 Research oriented structural and functional medical imaging ..................................................................... 11 Data Analysis in R............................................................................................................................................ 13 iCub programming .......................................................................................................................................... 15 Advanced Neurophysiology............................................................................................................................ 16 Introduction to non linear control theory ..................................................................................................... 17 Introduction to linear systems ....................................................................................................................... 19 Psychophysical methods................................................................................................................................. 20 Neurophysiology of the motor systems ......................................................................................................... 21 Design of Experiments .................................................................................................................................... 22 C++ programming techniques ........................................................................................................................ 23 Tissue Engineering: Cells, Biomaterials and Bioreactors .............................................................................. 24 Modeling neuronal structures: from single neurons to large-scale networks ............................................. 26 Public Speaking and Effective Communication Skills .................................................................................... 27 Introduction to Python programming ............................................................................................................ 29 Advanced microscopy methods ..................................................................................................................... 30 Non-linear excitation microscopy: from theory to tissue imaging ............................................................... 31 Bio-imaging at the single molecule level. ...................................................................................................... 32 Photo-physical mechanisms and dynamic investigations in super-resolution microscopy ......................... 33 Characterization of Polymeric Materials ....................................................................................................... 35 Nano-plasmonic devices: an introduction ..................................................................................................... 37 Nano-plasmonic devices: from fabrication to applications .......................................................................... 39 Laser-matter interactions: from fundamentals to applications .................................................................... 40 Virtual Prototyping Design ............................................................................................................................. 41 List of suggested Schools ................................................................................................................................ 43
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PhD Program in Bioengineering and Robotics - 2014
Available PhD Courses for 2014 Artificial Cognitive Systems .............................................................................................................................. 3
Regularization Methods for High Dimensional Machine Learning ................................................................. 5
Introductory design of mechatronic systems .................................................................................................. 6
Research oriented structural and functional medical imaging ..................................................................... 11
Data Analysis in R ............................................................................................................................................ 13
Introduction to non linear control theory ..................................................................................................... 17
Introduction to linear systems ....................................................................................................................... 19
Neurophysiology of the motor systems ......................................................................................................... 21
Design of Experiments .................................................................................................................................... 22
C++ programming techniques ........................................................................................................................ 23
Tissue Engineering: Cells, Biomaterials and Bioreactors .............................................................................. 24
Modeling neuronal structures: from single neurons to large-scale networks ............................................. 26
Public Speaking and Effective Communication Skills .................................................................................... 27
Introduction to Python programming ............................................................................................................ 29
List of suggested Schools ................................................................................................................................ 43
This course provides a comprehensive introduction to the emerging field of artificial cognitive systems. Inspired by artificial intelligence, developmental psychology, and cognitive neuroscience, the aim is to build systems that can act on their own to achieve goals: perceiving their environment, anticipating the need to act, learning from experience, and adapting to changing circumstances.
We develop a working definition of cognitive systems, one that strikes a balance between being broad enough to do service to the many views that people have on cognition and deep enough to help in the formulation of theories and models. We then survey the different paradigms of cognitive science to establish the full scope of the subject. We follow this with a discussion of cognitive architectures before tackling the key issues: autonomy, embodiment, learning & development, memory & prospection, knowledge & representation, and social cognition.
Syllabus
The course will be given over five days. There will be two 2-hour classes each day, plus
homework (4-5 hours)
1. The nature of cognition: models, definitions, autonomy, Marr’s levels of abstraction.
2. Paradigms of cognitive science: cognitivism and artificial intelligence, emergent systems,
connectionism, dynamical systems, enaction.
3. Cognitive architectures: cognitivist, emergent, and hybrid architectures, desirable
There will be a final examination decided by the instructor.
Reading List
Vernon, D. Artificial Cognitive Systems – A Primer, MIT Press (in press).
Venue
Istituto Italiano di Tecnologia, Via Morego 30, Bolzaneto, Genova
Course dates
20-24 October 2014
PhD Program in Bioengineering and Robotics - 2014
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Regularization Methods for High Dimensional Machine Learning Instructors Francesca Odone ([email protected]) Lorenzo Rosasco ([email protected]) DIBRIS - Department of Informatics, Bioengineering, Robotics, Systems Engineering
Credits: 5
Synopsis Understanding how intelligence works and how it can be emulated in machines is an age old dream and arguably one of the biggest challenges in modern science. Learning, its principles, and computational implementations are at the very core of this endeavor. Only recently we have been able, for the first time, to develop artificial intelligence systems that can solve complex tasks considered out of reach for decades. Modern cameras can recognize faces, and smart phones recognize people voice; car provided with cameras can detect pedestrians and ATM machines automatically read checks. In most cases at the root of these success stories there are machine learning algorithms, that is, softwares that are trained rather than programmed to solve a task. In this course, we focus on the fundamental approach to machine learning based on regularization. We discuss key concepts and techniques that allow to treat in a unified way a huge class of diverse approaches, while providing the tools to design new ones. Starting from classical notions of smoothness, shrinkage and margin, we cover state of the art techniques based on the concepts of geometry (e.g. manifold learning), sparsity, low rank, that allow to design algorithms for supervised learning, feature selection, structured prediction, multitask learning. Practical applications will be discussed. Syllabus Organization: 20 hours course including practical laboratory sessions. Exam: Final project or Wikipedia-style article. Program summary:
Introduction to Machine Learning
Kernels, Dictionaries and Regularization
Regularization Networks and Support Vector Machines
Class 3 (2h) Spectral analysis of ERSP. Peak analysis, clustering electrodes and averaging
time interval. Subject and group level analysis. Statistical analysis in EEGLAB and R.
Teacher Claudio Campus.
Class 4 (2h) Introduction to EEG source analysis. Theory, forward model and inverse
problem resolution. Differences between dipoles and distributed source analysis. Alternative
models, post processing approaches. Teacher Alberto Inuggi.
Class 5 (2h) Source analysis in Brainstorm. Teacher Alberto Inuggi.
Class 6 (2h) Statistical analysis in SPM. Comparison between EEG, fMRI and TMS tools.
Teacher Alberto Inuggi.
Class 7 (3h). Practical day on the RBCS’s EEG Tools analysis framework. Teacher Alberto
Inuggi and Claudio Campus.
There will be a final examination decided by the instructors.
Prerequisites
Good knowledge of Matlab environment and syntax.
Reading List
Allena M, Campus C, Morrone E, De Carli F, Garbarino S, Manfredi C, Sebastiano DR, Ferrillo F (2009). Periodic limb movements both in non-REM and REM sleep: relationships between cerebral and autonomic activities. Clin Neurophysiol., 120:1282-90. doi: 10.1016/j.clinph.2009.04.021. Epub 2009 Jun 7.
Babiloni F, Babiloni C, Carducci F, Romani GL, Rossini PM, Angelone LM, Cincotti F(2003). Multimodal integration of high-resolution EEG and functional magnetic resonance imaging data: a simulation study. Neuroimage 19, 1-15.
Campus C, Brayda L, De Carli F, Chellali R, Famà F, Bruzzo C, Lucagrossi L, Rodriguez G (2012). Tactile exploration of virtual objects for blind and sighted people: the role of beta 1 EEG band in sensory substitution and supramodal mental mapping. J Neurophysiol., 107:2713-29. doi: 10.1152/jn.00624.2011. Epub 2012 Feb 15.
Fuchs M, Wagner M, Kohler T, Wischmann HA (1999). Linear and nonlinear current density reconstructions. Adv Neurol 16, 267-295.
Inuggi A, Filippi M, Chieffo R, Agosta F, Rocca MA, González-Rosa JJ, Cursi M, Comi G, Leocani L (2010). Motor area localization using fMRI-constrained cortical current density reconstruction of movement-related cortical potentials, a comparison with fMRI and TMS mapping. Brain Res. 1308:68-78.
Inuggi A, Amato N, Magnani G, González-Rosa JJ, Chieffo R, Comi G, Leocani L (2011). Cortical control of unilateral simple movement in healthy aging. Neurobiol Aging 32, 524-538.
The Advanced Neurophysiology course is a series of lessons dedicated to an in-depth analysis of a few selected topics of Neurophysiology. In particular there will be lessons held by Prof. Becchio, Prof. Burr, Prof. Fadiga, Prof. Morasso, Prof. Morrone and Prof. Pozzo.
Instructors
Cristina Becchio, David Burr, Pietro Morasso, Concetta Morrone, Thierry Pozzo
Credits: 4
Syllabus Each lesson will last about two hours and will be held by one of the instructors. Duration of the course:
10 hours.
Venue
Istituto Italiano di Tecnologia, Via Morego 30, Bolzaneto, Genova
PhD Program in Bioengineering and Robotics - 2014
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Introduction to non linear control theory
Course at a Glance
The course aims at introducing methods for analysis and control of nonlinear systems. Particular attention
is paid to time domain analysis techniques, such as Lyapunov stability and feedback linearization.
Applications to robotic manipulators is also presented. The course is co-organized by the Robotics, Brain
and Cognitive Science Department, Istituto Italiano di Tecnologia (IIT).
The course Psychophysics Methods will briefly review the principal methods used in psychophysics to measure sensory thresholds and perceptions in general. The course is organized by the RBCS group at the Italian Institute of Technology.
Psychophysics investigates the relationship between stimuli in the physical domain and sensations or perceptions in the psychological domain. It provides a corpus of well-established methods to study and formulate models of perception. The course will start with a review of the history of psychophysics and of the principal results obtained in this field. Then, the course will present some psychophysical concepts (e.g., the concepts of sensory threshold and psychological scale) and describe classic and modern psychophysical methods to measure them. The students will have the opportunity to make simple psychophysical experiments in class to test their understanding of the methods.
Syllabus
total of 12 hours - each class is 2 hours.
class 1 (C1) History of psychophysics and concept of threshold. Teacher Monica Gori
class 2 (C2) Methods of threshold measurement (Method of constant stimuli, Methods of
limits, Methods of adjustment). Teacher Monica Gori
class 3 (C3) Methods of threshold measurement (Adaptative Methods). Teacher Monica
Gori
class 4 (C4) Signal Detection Theory. Teacher Gabriel Baud Bovy
class 5 (C5) Unidimensional scaling methods. Teacher Gabriel Baud Bovy
class 6 (C6) Item Response Theory. Teacher Gabriel Baud Bovy
There will be a final examination decided by the instructors.
Reading List
Psychophysics the fundamentals, George A. Gescheider
Venue
Istituto Italiano di Tecnologia, Via Morego 30, Bolzaneto, Genova
The course Neurophysiology of the motor systems will review the main functional characteristics and the anatomical and physiological bases of the motor systems. The course will also cover recent advances in selected topics. See the synopsis and the syllabus for more details. The course is organized by the RBCS group at the Italian Institute of Technology.
From birth, we interact with the world through our senses and movements. How the brain process and transform sensory signals and organize the motor output is a major research question in Experimental Psychology and Neuroscience. The goal of the course is to present the motor systems from the anatomical, physiological and functional points of view. A particular focus will be on how physical stimuli are transduced into sensory signals by our peripheral sensory apparatus, as well as how the motor hierarchy organizes complex behavior.
Syllabus
total of 8 hours - each class is 2 hours
class 1 (C1) Motor system I.
class 2 (C2) Motor system II.
class 3 (C3) Motor system III.
class 4 (C4) Probing the motor system with Transcranial Magnetic Stimulation.
There will be a final examination chosen by the instructor.
Reading List
Kandel, Eric R.; Schwartz, James Harris; Jessell, Thomas M. (2000) [1981], Principles of
Neural Science (Fourth ed.), New York: McGraw-Hill
Venue
Istituto Italiano di Tecnologia, Via Morego 30, Bolzaneto, Genova
Reading list [1] Physical properties of polymers handbook. James E. Mark. Springer. [2] Polymer Characterization: Physical Techniques, 2nd Edition. Dan Campbell, Richard A. Pethrick, Jim R. White. CRC Press.
Weekly homework will be assigned at the end of each lecture with an estimated average workload
of 3 hours per week. There will be a final examination decided by the instructor.
Prerequisites
Basic knowledge of classical physics and programming.
Reading List General references are:
- Klaus-Jurgen Bathe, Finite Element Procedures, Prentice-Hall of India, 2009
- Rajiv Rampalli, Gabriele Ferrarotti & Michael Hoffmann, Why Do Multi-Body
System Simulation?, NAFEMS Limited, 2011
- R.J.Del Vecchio, Design of Experiments, Hanser Understanding Books, 1997
Venue
Istituto Italiano di Tecnologia, Via Morego 30, Bolzaneto, Genova
Course dates April - May
PhD Program in Bioengineering and Robotics - 2014
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List of suggested Schools
Note that not all the schools take place every year
Annual School of Bioengineering School duration: 4 days CFs: 4 http://www.bioing.it/?q=node/35
IEEE-EMBS Summer School on Biomedical Signal Processing School duration: 1 week CFs: 5 http://www.eambes.org/events/ieee-embs-summer-school-on-biomedical-signal-processing
European Summer School of Neuroengineering School duration: 1 week CFs: 5 http://neuroengineering.it/
Advanced Course in Computational Neuroscience School duration: 4 weeks CFs: 15 http://fias.uni-frankfurt.de/de/accn/
The iCub Summer School School duration: 2 weeks CFs: 8 http://eris.liralab.it/summerschool/
School of Photonics 2014: “Seeing sharp and further with the optical microscope” School duration: 4 days CFs: 4 http://web.nano.cnr.it/scuolafotonica2014/