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B012521 - MODELS AND CALCULI FOR PHYSICS
Main information
Course Content
Suggested readings
Learning Objectives
Further information
Type of Assessment
Course program
Academic Year 2012-13
Coorte 2011 - Second Cycle Degree in COMPUTER SCIENCE
Course year
Second year - Second Semester
Belonging Department
Statistics, IT and its applications "G. Parenti" (DISIA)
Course Type
Single education field course
Scientific Area
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS
Credits
6
Teaching Hours
48
Teaching Term
04/03/2013 ⇒ 14/06/2013
Attendance required
No
Type of Evaluation
Final Grade
Course Content
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Course program
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Lectureship
Mutuality
Course teached as:
B014447 - RETI NEURALI
Second Cycle Degree in COMPUTER SCIENCE
B014447 - RETI NEURALI
Second Cycle Degree in COMPUTER SCIENCE
Course Content
Neural dynamics of animal brain. Perceptron. Optimum Bayes classifier. Multilayer Perceptron. Learning algorithms. Radial Basis Functions. Clustering and multi-scales radial basis functions. Analysis of patterns in time. Widrow-Hoff’s filters. Non-linear control problems. Kohonen network. Principal Component Analysis. Statistical mechanics method: Hopefield network and recursive neural networks. Basic concepts of Statistical Mechanics. Spin glasses and Hopfield model. Replica simmetry. Parisi
Suggested readings (Search our library's catalogue)
Lectures notes.
Wasserman, "Advanced methods in neural computing", Reinhold New York 1993.
Wasserman, "Advanced methods in neural computing", Reinhold New York 1993.
Learning Objectives
Educational Objectives:
The course aims to provide a deep knowledge of advanced computing techniques, used for data analysis of complex systems (es High Energy Physics). These models are inspired to “neural network” and try to reproduce some aspects of animal brain ability and its extraordinary efficiency of recognizing aggregated signals.
Acquired Competence:
Detailed knowledge of different models of artificial neural networks and their applications.
Acquired Skills (at the end of the course):
Construction of neural networks useful for data analysis using MATLAB or MATHEMATICA.
The course aims to provide a deep knowledge of advanced computing techniques, used for data analysis of complex systems (es High Energy Physics). These models are inspired to “neural network” and try to reproduce some aspects of animal brain ability and its extraordinary efficiency of recognizing aggregated signals.
Acquired Competence:
Detailed knowledge of different models of artificial neural networks and their applications.
Acquired Skills (at the end of the course):
Construction of neural networks useful for data analysis using MATLAB or MATHEMATICA.
Further information
Frequency of lectures, practice and lab: Recommended
Teaching Tools
UniFi E-Learning: http://e-l.unifi.it
Office Hours:
At the end of the lecture or by appointment (contact professor via e-mail).
Dipartimento di Fisica e Astronomia
Via Sansone, 1
50019 - Sesto Fiorentino (FI)
Tel: 055 4572000 - 2307622 - 2307722 Fax: 055 4572121
E-Mail: gregorio.landi@unifi.it, landi@fi.infn.it
Teaching Tools
UniFi E-Learning: http://e-l.unifi.it
Office Hours:
At the end of the lecture or by appointment (contact professor via e-mail).
Dipartimento di Fisica e Astronomia
Via Sansone, 1
50019 - Sesto Fiorentino (FI)
Tel: 055 4572000 - 2307622 - 2307722 Fax: 055 4572121
E-Mail: gregorio.landi@unifi.it, landi@fi.infn.it
Type of Assessment
Oral and project.
Course program
Neural dynamics of animal brain. Perceptron. Naive Bayes classifier. Multilayer Perceptron. Learning algorithms. Radial Basis Functions. Green functions. Clustering and multi-scales radial basis functions. Analysis of patterns in time. Widrow-Hoff’s filters. Non-linear control problems. Kohonen network. Principal Component Analysis. Statistical mechanics method: Hopefield network and recursive neural networks. Basic concepts of Statistical Mechanics. Spin glass: Isomorphic Hopefield model. The model of Sherrington and Kirkpatrick. Reply simmetry. Parisi solution (Replica Symmetry Breaking). Ultrametricity and neural network; ierarchical organization in a disordered system. Optimization: Simulated Annealing and Monte Carlo methods. Toolbox of MATLAB neural-networks.