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Argentina (1)Australia (1)Belgium (1)
Brazil (18)Czech Republic (1)Finland (1)
France (1)Greece (1)India (3)
Irland (2)Italy (5)Mexico (2)
Netherlands (1)New Zealand (1)Philippines (1)
Poland (1)Portugal (3)Romania (1)
Russian Federation (1)Saudi Arabia (1)Scotland (1)
Spain (10)Switzerland (1)Taiwan (1)
United Kingdom (4)United States (5)


Argentina    [Top]
  • Title of the course: Redes Neuronales y sus aplicaciones en Ingeniería
    Level: graduate
    Institute and departament: Universidad Tecnológica Nacional - Facultad Regional Santa Fe. Departamento Sistemas
    Short description of topics: Historical perspective. Biological comparison. The perceptron and multilayer perceptron. The radial basis function networks. The self organizing map. Recurrent networks. Learning rules. Supervised and unsupervised learning. Applications.
    Lecturer or responsible person: Dra. Georgina Stegmayer
    Other people involved: Dra. Milagros Gutiérrez
    Language: Spanish
    Web page: http://www.frsf.utn.edu.ar/33-Ingenieria-en-Sistemas.html
    Starting year of the course in its present form: 2004
    Goals/contents of the course: * Formar competencias y habilidades en el campo de las Redes Neuronales. * Presentar una válida herramienta de análisis y modelado de problemas prácticos en Ingeniería. * Presentar y discutir los principales tipos de redes neuronales y sus correspondientes reglas de aprendizaje.
    Text book or classnotes: http://www.frsf.utn.edu.ar/matero/visitante/index.php?id_catedra=78&ver=7
    Slides or others supporting material: http://www.frsf.utn.edu.ar/matero/visitante/index.php?id_catedra=78&ver=4
    Duration and period: 4 months, august to november
    Approximate number of students: 10
    Intended audience: last year computer science students
    The course is part of: B.S. in Systems Engineer
    Type: elective

Australia    [Top]
  • Title of the course: Data Mining
    Level: Postgraduate
    Institute and departament: CQUniversity, School of Information and Communication Technology
    Short description of topics: Classification, Regression, Association Rule Mining, Text Mining, Neural Networks, Decision Tree, Support Vector Machine, Apriori.
    Lecturer or responsible person: Shawkat Ali
    Other people involved: Ian Moore, Neville Richter, Ahmad Saeed, Glen Thorpe, SANTOSO WIBOWO, Saleh Wasimi
    Language: English
    Web page: http://sites.google.com/site/shawkat69/home
    Starting year of the course in its present form: 2005
    Goals/contents of the course: Data mining is the process of finding useful patterns in data, and this course examines the basics of data mining, model building and testing, and interpreting and validating results. Appropriate software is used by students to implement these ideas in practice. Students experience the theoretical and practical aspects of data mining.
    Text book or classnotes: http://www.cengage.com/cgi-wadsworth/course_products_wp.pl?fid=M20bB&product_isbn_issn=9780170136761&discipline_number=3072
    Duration and period: 6 Months Course
    Approximate number of students: 300
    The course is part of: MIT
    Type: Elective
    Additional information: You welcome to visit: http://sites.google.com/site/shawkat69/home

Belgium    [Top]
  • Title of the course: Modelling of Fuzziness and Uncertainty
    Level: undergraduate
    Institute and departament: Department of Applied Mathematics and Computer Science; Ghent University
    Short description of topics: Auxiliary order structures (poset, lattice, Boolean algebra, Morgan algebra, Kleene algebra), Zadeh´s fuzzy sets, alternative operations on fuzzy sets, triangular norms and conorms, flou set theory, L-flou set theory, L-fuzzy set theory, representation theorem of Negoita and Ralescu, cartesian product, typical membership functions, linguistic variables and linguistic hedges, calculus of level sets, Zadeh´s extension principle, bounded fuzzy sets in Rn, convex fuzzy sets in Rn, indices of fuzziness, calculus of fuzzy quantities
    Lecturer or responsible person: Martine De Cock
    Language: Flemish
    Goals/contents of the course: In the end students should be familiar with the basic concepts and techniques from fuzzy set theory and related models of uncertainty, among them L-fuzzy set theory and flou set theory. The students should be ready to start more advanced courses offered in the master of computer science and the master of applied mathematics.
    Duration and period: 1st semester
    Approximate number of students: 40
    Intended audience: senior undergraduates in mathematics
    The course is part of: B.S. in Mathematics

Brazil    [Top]
  • Title of the course: Business Intelligence Master
    Level: Graduate
    Institute and departament: Pontifícia Universidade Católica- Rio de Janeiro
    Short description of topics: Decision Support Systems, Business Intelligence, Neural Networks, Fuzzy Logic, Genetic Algorithms, Data Mining, Scheduling, Decision support for uncertainties, Human Reliability, Support Statistic Methods, Decision Support Intelligence System Project.
    Lecturer or responsible person: A.Cruz, C.Aranha, D.Szwarcman, J.Domech Moré, J.Lazo, K.Figueiredo, M.Dias, M.Pacheco, M.Vellasco, M.Barros, R.Tanscheit
    Language: Portuguese
    Web page: http://bimaster.ica.ele.puc-rio.br/Home/Index.rails
    Starting year of the course in its present form: Spring 2007
    Goals/contents of the course: To promote a sound training of skilled professionals, able to participate in activities of design, design, development, maintenance, management, administration and to promote the use of methods and intelligent systems for decision support in general.
    Text book or classnotes: http://bimaster.ica.ele.puc-rio.br/notes/index.rails?name=Notas%20de%20Aulas
    Slides or others supporting material: http://bimaster.ica.ele.puc-rio.br/summaries/index.rails?name=Ementas
    Duration and period: 12 months, plus 3 months to finish final paper
    Approximate number of students: 40
    Intended audience: Students interested in learning new techniques, decision support systems DSS), and applying calculations to real-life problems
    The course is part of: graduate course (specialization)
    Additional information: http://bimaster.ica.ele.puc-rio.br/Home/Index.rails or e-mail your questions to bi-master.contato@ele.puc-rio.br
  • Title of the course: Data Mining
    Level: postgraduate
    Institute and departament: UFPA-PPGEE
    Short description of topics: Initial considerations, objectives, characteristics and applications in the area of data mining. Artificial intelligence and knowledge discovery in database. Data analysis and preprocessing. Data mining tasks: classification, clustering, association and prediction. Basic principles and applications of classic data mining algorithms: decision trees and rules, association rules, neural networks, Bayesian networks, clustering, regression, etc. Analysis and interpretation of results. Preparation and presentation of projects.
    Lecturer or responsible person: Adamo Santana
    Language: Portuguese
    Starting year of the course in its present form: 2009
    Goals/contents of the course: Introduce the basic concepts of data mining, focusing on the main machine learning algorithms. Show the key tasks and techniques of data mining. Demonstrate the development of data mining in practical and real-world applications.
    Duration and period: One semester
    Approximate number of students: 30
    Intended audience: Postgraduate students
    The course is part of: No
    Type: Elective
  • Title of the course: Design of Innovation with Genetic Algorithms
    Level: graduate
    Institute and departament: Institute of Mathematical and Computer Sciences
    Short description of topics: Properties for the success of competent genetic algorithms. Selectorecombinative genetic algorithms and facetwise models for theoretical analysis. Deduction of theoretical models for: population size, building blocks growing; convergence time, drift time, probabilistic choice of building blocks, control maps and mixing/innovation boundary. Principles of “linkage learning”.
    Lecturer or responsible person: Alexandre Claudio Botazzo Delbem
    Language: Portuguese
    Starting year of the course in its present form: 2009
    Duration and period: 24 hours, from October to November
    Approximate number of students: 15
    Intended audience: graduate students interested in theoretical aspects of Evolutionary Computation
  • Title of the course: Estimation of Distribution Algorithms
    Level: graduate
    Institute and departament: Institute of Mathematical and Computer Sciences
    Short description of topics: Methods of Probabilistic Model Construction. Extended Compact Genetic Algorithm. Bayesian Optimization Algorithm, hierarchical Bayesian Optimization Algorithm, Search-space Reduction Algorithms.
    Lecturer or responsible person: Alexandre Claudio Botazzo Delbem
    Language: Portuguese
    Starting year of the course in its present form: 2008
    Duration and period: 24 hours, from August to September
    Approximate number of students: 15
    Intended audience: graduate students interested in Evolutionary Computation
  • Title of the course: Evolutionary Computation
    Level: postgraduate
    Institute and departament: University of Pernambuco / School of Engineering
    Short description of topics: Theoretical overview of main techniques and several practical hands-on projects
    Lecturer or responsible person: Prof. Fernando Buarque
    Other people involved: Prof. Carmelo Bastos
    Language: Portuguese
    Web page: in construction
    Starting year of the course in its present form: second semester
    Goals/contents of the course: -Further theoretical understanding of techniques; -Development of CI practical skills (towards real-world applications); -Increase awareness of CI potentials in solving complex problems.
    Text book or classnotes: Classnotes are being compiled into a book
    Slides or others supporting material: Being revamped
    Duration and period: One semester
    Approximate number of students: 10 postgrads + 20 undergrads (invited)
    Intended audience: Masters students and above
    The course is part of: Yes it is (undergrads are invited)
    Type: Compulsory (postgrads)
    Additional information: Postgrad students are team leaders for undergrads, mainly during projects.
  • Title of the course: Evolutionary Computation
    Level: Graduate
    Institute and departament: Pontifícia Universidade Católica- Rio de Janeiro, Department of Electric Engineering
    Short description of topics: Basic Concepts, Evolution and Natural Selection; Components of an AG; Population size; Methods of Reproduction, Selection, Mutation and Crossover; Traditional AG; Prisoners' Dilemma (Machine Learning); Mathematical Foundations of GAs; Schema Theory; Deceptive AG (Deceptive) and Epistasis; Problems of Combinatorial Optimization; Optimization of Planning; Introduction to Genetic Programming; Introduction to Evolutionary Hardware; Environments and Programming Techniques of GAs; Parallelization of GAs; Applications.
    Lecturer or responsible person: Marco Aurélio Pacheco
    Language: Portuguese
    Duration and period: 1 semester (March- July or August- December)
    Approximate number of students: 5 to 40, depending on the year
    Intended audience: Students interested in computational intelligence courses and methods of decision support systems
    The course is part of: graduate course (specialization)
    Type: compulsory/elective
  • Title of the course: Evolutionary Computation
    Level: Graduate
    Institute and departament: Federal University of Pará - Electrical Engineering Graduate
    Short description of topics: Basics of genetic algorithms (encoding, operators, selection mechanisms, theoretical foundations, parallelism), and evolution strategies, evolutionary programming and genetic programming
    Lecturer or responsible person: Roberto Célio Limão de Oliveira
    Other people involved: Otávio Noura Teixeira
    Language: Portuguese
    Starting year of the course in its present form: 2002
    Duration and period: 1 semester
    Approximate number of students: 25
    Intended audience: Undergraduates students in engineering and computer science
    The course is part of: MS in Computer Science and MS/PhD in Electrical Engineering
    Type: compulsory
  • Title of the course: Evolutionary Systems Applied to Robotics
    Level: undergraduate
    Institute and departament: Institute of Mathematical and Computer Sciences
    Short description of topics: Canonical evolutionary algorithms. System modeling (representations, mono- and multi-objective formulations). Case studies of evolutionary algorithms applied to robotic systems.
    Lecturer or responsible person: Alexandre Claudio Botazzo Delbem
    Other people involved: Eduardo do Valle Simoes
    Language: Portuguese
    Starting year of the course in its present form: 2009
    Duration and period: 32 hours (from August to November)
    Approximate number of students: 30
    Intended audience: undergraduate students interested in evolutionary computation or robotics
    The course is part of: Yes
    Type: elective
  • Title of the course: Fuzzy Logic
    Level: Graduate
    Institute and departament: Pontifícia Universidade Católica- Rio de Janeiro, Department of Electric Engineering
    Short description of topics: Definitions: Basic Characteristics, Types of Uncertainty; The Rubik's Cube; Fuzzy Sets; Properties and Characteristics of Fuzzy Sets; Formats Fuzzy Sets; Logical Operations on Fuzzy Sets; Settings t-norm and t-conorm; Hedges; Fuzzy Relations and Compositions; Traditional Logic: Modus ponens and Modus Tollens; Fuzzy Logic: Generalized modus ponens; Fuzzy Systems; Rule Base, Inference Modules, Fuzzificaion, Defuzzification; Fuzzy control; Applications
    Lecturer or responsible person: Ricardo Tanscheit and Marley Maria
    Language: Portuguese
    Duration and period: 1 semester (March- July or August- December)
    Approximate number of students: 5 to 40, depending on the year
    Intended audience: Students interested in computational intelligence courses and methods of decision support systems
    The course is part of: M.A. in Electric Engineering
    Type: compulsory/elective
  • Title of the course: Fuzzy Systems
    Level: graduate
    Institute and departament: Computing Department - Federal University of São Carlos
    Short description of topics: Fuzzy sets and fuzzy logic; Approximate Reasoning; Rule-based Computations; Fuzzy Systems Modeling; Clustering methods; Genetic Algoritms; Genetic Fuzzy Systems.
    Lecturer or responsible person: Heloisa de Arruda Camargo
    Language: protuguese
    Starting year of the course in its present form: 2002
    Goals/contents of the course: The goal of the course is to provide a theoretical background for graduate students both at masters or doctoral level in computational intelligence, as well as the necessary knowledge to those students that are willing to develop their research work specifically in one of the topics studied in the course.
    Slides or others supporting material: http://www2.dc.ufscar.br/~heloisa/SN2007/SN.htm
    Duration and period: one semester, from august to november
    Approximate number of students: 10 per year
    Intended audience: graduate students, masters or doctorate
    The course is part of: Graduate Program in Computer Science - Federal University of São Carlos
    Type: elective
  • Title of the course: Inteligência Computacional
    Level: undergraduate
    Institute and departament: COPPE/UFRJ
    Short description of topics: Soft computing techniques applied to data modeling.
    Lecturer or responsible person: Alexandre Evsukoff
    Other people involved: Nelson F. F. Ebecken
    Language: Protuguese
    Web page: http://www.poli.ufrj.br/graduacao_cursos_engenharia_computacao_informacao.php
    Starting year of the course in its present form: 2008
    Goals/contents of the course: - Linear models for classification and regression - Introduction to fuzzy logic and fuzzy systems - Introduction to neural networks - Introduction to support vector machiines
    Text book or classnotes: http://www.support-vector.ws/
    Duration and period: 15 weeks
    Approximate number of students: 30
    Intended audience: undergraduate
    The course is part of: B.S. in Computer Engineering
    Type: compulsory
  • Title of the course: Introduction to Computational Intelligence
    Level: Undergraduate
    Institute and departament: Fluminense Federal University - Electrical Engineering Department
    Short description of topics: Intelligent Agents, Probabilistic Methods, Optimization, Genetic Algorithms, Learning Theory, Neural Networks
    Lecturer or responsible person: Prof. Vitor Hugo Ferreira, D.Sc.
    Language: Portuguese
    Starting year of the course in its present form: 2010
    Approximate number of students: 10
    The course is part of: Electrical Engineering
    Type: Elective
  • Title of the course: Introduction to Evolutionary Systems
    Level: graduate
    Institute and departament: Institute of Mathematical and Computer Sciences
    Short description of topics: Fundamental aspects of algorithms based on population evolution: genetic algorithms, evolutionary strategies, genetic programming, micro genetic algorithm, simulated annealing, univariate model distribution algorithm, etc. The main features of algorithms based on colony behavior: ant colony optimization, particle swarm optimization, artificial bee colony, honey-bee mating algorithm, shuffled frog leaping, fish school search, bacterial foraging optimization, etc.
    Lecturer or responsible person: Alexandre Claudio Botazzo Delbem
    Language: Portuguese
    Duration and period: 21 hours, from March to April
  • Title of the course: Multi-Objective Optimization
    Level: Postgraduate
    Institute and departament: University of Pernambuco / School of Engineering
    Short description of topics: Theoretical overview of main techniques and several practical hands-on projects. The approaches considered in the course are based on Evolutionary Computation and Swarm Intelligence.
    Lecturer or responsible person: Carmelo J. A. Bastos Filho
    Other people involved: Fernando Buarque de Lima Neto
    Language: Portuguese
    Web page: in construction
    Starting year of the course in its present form: 2007
    Goals/contents of the course: -Further theoretical understanding of techniques; -Development of CI practical skills (towards real-world applications); -Increase awareness of CI potentials in solving complex problems.
    Duration and period: one semester
    Approximate number of students: 8
    Intended audience: Masters students and above
    The course is part of: MSc. in Computer Science
    Type: elective
  • Title of the course: Neural Networks
    Level: Graduate
    Institute and departament: Pontifícia Universidade Católica- Rio de Janeiro, Department of Electric Engineering
    Short description of topics: Understanding of Design and Manufacture of Integrated Circuit (IC); Project Methodology; Basic Characteristics: Learning, Association, Generalization and Robustness; History; Structure of Artificial Neuron; Interconnect Structures; Types of Learning - Supervised and unsupervised; Learning algorithms: Perceptron, Delta Rull, Back Propagation, Hopfield Network, Bidirectional Associative Memories, Self-Organizing Networks, Probabilistic Networks and Networks of Radial Basis Function; Temporal Networks (TDNN); Applications.
    Lecturer or responsible person: Marley Vellasco
    Language: Portuguese
    Duration and period: 1 semester (March- July or August- December)
    Approximate number of students: 5 to 40, depending on the year
    Intended audience: Students interested in computational intelligence courses and methods of decision support systems
    The course is part of: M.A. in Electric Engineering
    Type: compulsory/elective
  • Title of the course: Neural Networks II
    Level: Graduate
    Institute and departament: Pontifícia Universidade Católica- Rio de Janeiro, Department of Electric Engineering
    Short description of topics: Issues of Generalization; Boundaries of architecture and Relation to Data Available; Adjustment and Adaptation of Neural Networks; Significance of Variables; Interconnect Structures; Self Organizing Hybrid Model; Support Vector Machines; Reinforcement Learning; Principal Component Analysis
    Language: Portuguese
    Duration and period: 1 semester (March- July or August- December)
    Approximate number of students: 5 to 40, depending on the year
    Intended audience: Students interested in computational intelligence courses and methods of decision support systems
    The course is part of: M.A. in Electric Engineering
    Type: compulsory/elective
  • Title of the course: Pattern Classification
    Level: Graduate
    Institute and departament: Pontifícia Universidade Católica- Rio de Janeiro, Department of Electric Engineering
    Short description of topics: • Introduction: Motivation, Key Applications, Features and Feature Vectors, Supervised and unsupervised classification; • Classifiers Based on Bayesian Decision Theory: Bayesian decision theory, discriminant functions and Surfaces makers, the Case of Normal Distribution; • Supervised Methods: Learning Bayesian Classifier Bayes and Maximum Likelihood Estimation, Classification and Capacity Issues Dimensionality, Window Type Methods parzen Verses 'Nearest Neighbor', Multiple Discriminant Fisher's discriminant functions Generalised Perceptron Algorithm, Non-separable Behavior, and Pseudo Inverse Least Squares, Relation to Discriminant Fisher, Widrow-Hoff Process and Methods of Stochastic Approximation; • Unsupervised Methods: Mixture Densities, Bayesian Learning Not Supervised, Similarity Measures, Iterative Methods for unsupervised classification, Kohonen and Hybrid Methods
    Language: Portuguese
    Duration and period: 1 semester (March- July or August- December)
    Approximate number of students: 5 to 40, depending on the year
    Intended audience: Students interested in computational intelligence courses and methods of decision support systems
    The course is part of: M.A. in Electric Engineering
    Type: compulsory/elective
  • Title of the course: Swarm Intelligence
    Level: postgraduate
    Institute and departament: University of Pernambuco / School of Engineering
    Short description of topics: Theoretical overview of main techniques and several practical hands-on projects. Among others, we can cite the following techniques: Particle Swarm Optimization, Ant Colony Optimization, Fish School Search, Glowworm Swarm Optimization, Bacterial Foraging Optimization, Artificial Bee Colony Optimization.
    Lecturer or responsible person: Carmelo J. A. Bastos Filho
    Other people involved: Fernando Buarque de Lima Neto
    Language: Portuguese
    Web page: in construction
    Starting year of the course in its present form: 2007
    Goals/contents of the course: -Further theoretical understanding of techniques; -Development of CI practical skills (towards real-world applications); -Increase awareness of CI potentials in solving complex problems.
    Text book or classnotes: Classnotes are being compiled into a book.
    Duration and period: One semester
    Approximate number of students: 6
    Intended audience: Masters students and above
    The course is part of: MSc. in Computer Science
    Type: elective

Czech Republic    [Top]
  • Title of the course: Introduction to mathematical methods for artificial intelligence
    Level: Undergraduate
    Institute and departament: University of Ostrava, Institute for Research and Applications of Fuzzy Modeling
    Short description of topics: Main directions in AI. Soft Computing from the AI perspective. Neural networks, fuzzy modeling, particle swarm optimization.
    Lecturer or responsible person: Martin Stepnicka
    Language: Czech (English)
    Web page: http://irafm.osu.cz
    Intended audience: Bachelor/Master students of mathematics and informatics

Finland    [Top]
  • Title of the course: Evolving Intelligent Systems
    Level: Postgraduate
    Institute and departament: Aalto University School of Science and Technology (former Helsinki University of Technology), Dpt. of Information and Computer Science.
    Short description of topics: Evolving intelligent systems with an emphasis on rule-based. Methodological aspects, evolving rule-based systems, evolving clustering, applications in different areas, including process industry, autonomous systems and signal processing.
    Lecturer or responsible person: Federico Montesino
    Language: English
    Web page: https://noppa.tkk.fi/ (not active yet)
    Starting year of the course in its present form: 2010
    Text book or classnotes: Evolving Intelligent Systems. Methodology and Applications, P. Angelov, D. Filev and N. Kasabov (Eds.). IEEE Press Series on Computational Intelligence. Wiley, 2010.
    Duration and period: Sept.-Dec. 2010
    Approximate number of students: 10
    Intended audience: Mainly MSc students at the department but open for other MSc programs and shared programs with University of Helsinki.
    The course is part of: Degree Programme of Computer Science and Engineering, MSc in Machine Learning and Data Mining, and others.
    Type: Elective, seminar course.

France    [Top]
  • Title of the course: Computational Intelligence
    Level: Post graduate
    Institute and departament: INSA Lyon, MSIS Program
    Short description of topics: I. Intelligent Systems What is computational intelligence? II. Fuzzy Logic Fuzzy sets, fuzzy reasoning using fuzzy if-then rules. Fuzzy modeling Designing a Fuzzy expert system III. Neural Networks Mathematical modeling of neurons, perceptrons and its limitations. Backpropagation learning algorithm and its limitations. Unsupervised neural networks, clustering algorithms. Designing neural networks IV. Evolutionary Computation How Evolutionary Computation Works? Evolutionary programming, evolution strategies, genetic programming. Designing simple genetic algorithms for optimizing objective functions Swarm Intelligence V. Hybrid Intelligent Systems Why hybrid systems? Architecture of a neuro-fuzzy system. Evolutionary neural networks, evolutionary fuzzy systems and some hybrid frameworks. VI. Data Mining Basic concepts of data mining and knowledge discovery. CI techniques for data mining. VII. Application Case Study Nonlinear system modeling, pattern recognition, financial modeling, multi-criteria decision making, data mining, Internet modeling etc.
    Lecturer or responsible person: Ajith Abraham
    Other people involved: -
    Language: English
    Starting year of the course in its present form: 2008
    Goals/contents of the course: This advanced course on computational intelligence introduces all the core components of modern intelligent systems with a focus on designing hybrid systems. Students will have hands on experience with some of the tools like neural networks, fuzzy systems, swarm intelligence and evolutionary computation. Students will have the opportunity to perform some research on applying hybrid approaches for practical problem solving.
    Text book or classnotes: http://www.softcomputing.net/tutorial.html
    Duration and period: Lectures: 30 hours, Practicals: 20 hours
    Approximate number of students: 10
    Intended audience: Students of Master of Science in Information Systems
    The course is part of: MSIS
    Type: Elective
    Additional information: Style mode of teaching: Lecture, outside readings, research. Examination schedule: Final Exam 40 % Assignments 20% Research report (conference paper standard) 40%

Greece    [Top]
  • Title of the course: Artificial and Computational intelligence
    Level: Undergraduate
    Institute and departament: University of Piraeus, Department of Industrial Management & Technology
    Short description of topics: Natural, artificial and computational intelligence. Symbolic- and sub-symbolic-level of representation. Artificial neural networks, genetic algorithms, fuzzy inference systems and their applications to real-world problems.
    Lecturer or responsible person: Tatiana Tambouratzis
    Language: Greek/English
    Web page: http://www.tex.unipi.gr/undergraduate/notes/ai/main.htm
    Starting year of the course in its present form: 2009
    Goals/contents of the course: Natural intelligence as an inspiration for artificial and computational intelligence. The limitations of directly mimicking natural intelligence: scaling problems, the importance of the framework (environment, materials etc.) of naturally intelligent action. The notion of computational complexity. The symbolic- and sub-symbolic-levels of representation. Artificial neural networks, genetic algorithms, fuzzy inference systems and their applications to real-world problems.
    Text book or classnotes: Tutor's notes
    Slides or others supporting material: http://www.tex.unipi.gr/undergraduate/notes/ai/main.htm
    Duration and period: One semester
    Approximate number of students: 10
    Intended audience: Fourth-year undergraduate students
    The course is part of: B.Sc. in Industrial Management & Technology
    Type: Elective
    Additional information: This course can be complemented by a fourth-year dissertation in the fields of artificial neural networks, genetic algorithms, fuzzy inference systems and their applications to real-world problems.

India    [Top]
  • Title of the course: Artificial Intelligence and Soft Computing
    Level: graduate
    Institute and departament: Dept. of Electronics and Tele-communication Engineering, Jadavpur University, Calcutta
    Short description of topics: Reasoning, Machine Learning, Intelligent Search, Planning, Visual and Linguistic Perception, Basis of Fuzzy Sets for Approximate Reasoning Neural Nets in Machine Learning Swarm and Evolutionary Optimization Techniques, Case Study 1: Criminal Investigation Case Study 2: Intelligent Robotics
    Lecturer or responsible person: Prof. AMIT KONAR
    Other people involved: Dr. ARUNA CHAKRABORTY
    Language: ENGLISH
    Starting year of the course in its present form: 1994
    Goals/contents of the course: The course is meant for M.Tech. students of any engineering discipline as a prerequiste for their research program for graduate level thesis/dissertation work.
    Text book or classnotes: Artificial Intelligence and Soft Computing, CRC Press, Boca Raton, Florida.
    Duration and period: One semester
    Approximate number of students: 120
    Intended audience: M.Tech students of EE, ETCE, CSE, Bio-medical Engg. and Instrumentation Engg.
    The course is part of: M.S. in Computer Science
    Type: Elective Core
  • Title of the course: Certificate Course on Machine Intelligence and Soft Computing
    Level: Postgraduate
    Institute and departament: Center for Soft Computing Research, Indian Statistical Institute, Kolkata
    Short description of topics: Pattern recognition; Image processing; Fuzzy sets; Neural networks; Evolutionary computation; Project work on real life problems.
    Lecturer or responsible person: S. K. Pal
    Other people involved: A. Ghosh, D.P. Mandal, M.K. Kundu, C.A. Murthy, S. Mitra, B. Chanda, P. Maji, K. Ghosh
    Language: English
    Web page: http://www.isical.ac.in/~scc
    Starting year of the course in its present form: August 2007
    Goals/contents of the course: This is a value addition course for post graduate degree holders. The objective of the program is to train the students with the scientific knowledge in soft computing and machine learning paradigm. This may help the students for getting suitable job or accelerating research activities.
    Duration and period: 6-8 months.
    Approximate number of students: 20
    Additional information: Visit the website: http://www.isical.ac.in/~scc or email qreries to: scc@isical.ac.in
  • Title of the course: Computational Intelligence
    Level: graduate
    Institute and departament: Dept. of Electronics and Tele-communication Engineering, Jadavpur University, Calcutta
    Short description of topics: Fuzzy Sets and Logic, Fuzzy Control, Fuzzy Pattern Recognition, Fuzzy Databases, Supervised Neural Learning, Unsupervised Learning, Reinforcement Learning, Competetive Learning, Support Vector Machine Classifier, Evolutionary Computing, Genetic Algorithm, Particle Swarm Optimization Algorithm, Differential Evolutionary Algorithm, Artificial Immune Systems, Ant Colony Optimization, Hybridization of Computational Intelligence Models/Algorithms,
    Lecturer or responsible person: Prof. AMIT KONAR
    Other people involved: Dr. ANANDA S. CHOWDHURY, Dr.ARUNA CHAKRABORTY, Dr. SWAGATAM DAS
    Language: English
    Starting year of the course in its present form: 2007
    Goals/contents of the course: The primary aim of this course is to employ Computational Intelligence models in engineering problem solving. Research initiative undertaken by students in this new discipline is also strengthened with the theoretical underpinnings offered in this course.
    Text book or classnotes: Computational Intelligence: Principles, Techniques and Applications, Springer, 2006.
    Duration and period: one semester
    Approximate number of students: 75
    Intended audience: EE, ECE, CSE, IT, Instrumentation Engg, students parsuing their M.S. degree
    The course is part of: M.S. in Computer Science
    Type: Elective

Irland    [Top]
  • Title of the course: Natural Computing
    Level: undergraduate/postgraduate
    Institute and departament: School of Computer Science and Informatics, University College Dublin
    Short description of topics: The field of Natural Computing has advanced rapidly over the past decade. One offshoot of this progress has been the development of a large family of algorithms inspired by Nature, including Biological, Social and Physical systems. Broadly speaking, these algorithms draw metaphorical inspiration from diverse sources, including the operation of biological neurons, processes of evolution, models of social interaction amongst organisms, and natural immune systems, in order to develop tools for solving real-world problems. This module provides an introduction to a broad range of Natural Computing algorithms and illustrates how they can be applied to real-world problems using a series of case studies. In addition to teaching the essentials of Natural Computing, the module provides experience in the planning, executing, writing up, and critical evaluation of research.
    Lecturer or responsible person: Michael O'Neill
    Language: English
    Web page: http://ncra.ucd.ie/COMP30290/
    Starting year of the course in its present form: 2006
    Goals/contents of the course: On completion of the module students should be able to: * Outline the main Natural Computing algorithms * Compare and Contrast the different Natural Computing methods * Solve a problem using Natural Computing * Design an experiment in Natural Computing * Write and critically review an academic paper
    Text book or classnotes: http://www.springer.com/computer/theoretical+computer+science/book/978-3-540-26252-7 and http://www.springer.com/computer/ai/book/978-1-4020-7444-8 and http://www.springer.com/engineering/book/978-3-642-00313-4
    Duration and period: 1 Semester
    Approximate number of students: 30
    The course is part of: BSc Computer Science
    Type: Elective
  • Title of the course: Natural Computing and Applications
    Level: Postgraduate
    Institute and departament: School of Computer Science and Informatics, University College Dublin
    Short description of topics: Offered on UCD's MSc Negotiated Learning Computer Science & Structured PhD Elective This module provides an introduction to a broad range of Natural Computing algorithms and illustrates how they can be applied to real-world.. .problems using a series of case studies. The module also provides experience in the planning, executing, writing up, and critical. evaluation of research. In addition, this 10 credit module focuses on the final step of Innovation where the research is brought to bear on real world problems, and examines Innovation opportunities.
    Lecturer or responsible person: Michael O'Neill
    Language: English
    Web page: http://ncra.ucd.ie/COMP41190/
    Starting year of the course in its present form: 2010
    Text book or classnotes: http://www.springer.com/3-540-26252-0 and http://www.springer.com/1-4020-7444-1 and http://www.springer.com/978-3-642-00313-4
    Duration and period: 1 Semester
    Approximate number of students: 30
    The course is part of: MSc Computer Science
    Type: Elective

Italy    [Top]
  • Title of the course: Curriculum in "Computational Intelligence"
    Level: undergraduate
    Institute and departament: University of Bari, Department of Informatics
    Short description of topics: Foundations of Computational Intelligence (Fuzzy Logic, Neural Networks, Genetic Algorithms); Advanced numerical techniques; Cognitive modelling, Image processing.
    Lecturer or responsible person: Anna M. Fanelli
    Other people involved: Corrado Mencar, Giovanna Castellano, Ciro Castiello, Nicoletta del Buono, Laura Caponetti
    Language: Italian
    Web page: http://informatica.uniba.it/laurea_magistrale/Intelligenza%20computazionale.pdf
    Starting year of the course in its present form: 2010
    Goals/contents of the course: The curriculum is aimed at forming specialists capable of analyzing, designing and developing complex systems with Computational Intelligence methodologies. In particular, the curriculum has the main objective of providing for theoretical, methodological and technological skills to design systems with human-like features, such as learning, reasoning and fault tolerance with imprecise and incomplete knowledge.
    Duration and period: 3 months, October-December
    Approximate number of students: 10
    Intended audience: Undergraduate students (last year), Ph.D. Students
    The course is part of: MS in Informatics
    Type: elective
  • Title of the course: Evolutionary Algorithms for Security
    Level: undergraduate
    Institute and departament: University of Catania, IPPARI Research Center
    Short description of topics: - a brief description on Computer Security notions. - a brief explanation on computational complexity theory; search spaces and optimization techniques; - Genetic Algorithms and Genetic Programming; Artificial Immune Systems; Swarm Intelligence; - presentation of some published work on Security based on nature-inspired methodologies.
    Lecturer or responsible person: Mario Pavone
    Language: italian language
    Starting year of the course in its present form: 3
    Approximate number of students: 20
    The course is part of: B.S. in Applied Computer Science
  • Title of the course: Intelligent Systems
    Level: graduate (MS)
    Institute and departament: Universita' degli Studi di Milano, Department of Information Technology
    Short description of topics: The course presents methodologies and techniques to implement intelligent systems for processing information and knowledge, i.e., systems which behaves like the human brain by employing computational intelligence approaches. In particular, the following main approaches will be studied: neural networks, fuzzy systems, and evolutionary computing.
    Lecturer or responsible person: Prof. Vincenzo Piuri
    Language: Italian
    Web page: http://www.dti.unimi.it/piuri
    Starting year of the course in its present form: 2009
    Goals/contents of the course: • Neural networks: Definitions. Neurons: structures, perceptrons, RBF. Neural topologies: feed-forward, feedback, SOM. Learning: supervised, unsupervised. Performance. Optimization. Classification and clustering. Associative memories. Prediction. Function approximation. Applications. • Fuzzy logic and systems: Fuzzy sets. Membership functions. Fuzzy rules. Defuzzification. Fuzzy reasoning. Fuzzy systems. Rough sets. Performance. Applications. • Evolutionary computing: Genomic representation. Fitness functions. Selection. Genetic algorithms. Genetic programming. Evolutionary programming. Evolutionary strategies. Differential evolution. Swarm intelligence. Artificial immune systems. • Hybrid systems
    Duration and period: semester, oct.-dec.
    Approximate number of students: 15
    Intended audience: MS students in computer science (curricula: information systems, industrial informatics, systems and network security)
    The course is part of: MS in Computer Science
    Type: compulsory
  • Title of the course: Natural Computation
    Level: graduate
    Institute and departament: University of Catania, Department of Mathematics and Computer Science
    Short description of topics: - explanation on computational complexity theory, and some NP-complete problems; - search spaces and optimization techniques - Evolutionary Strategy; Genetic Algorithms; Genetic Programming; Artificial Immune Systems; Swarm Intelligence and Ant Colony Systems; - a brief, and quickly explanation on Learning Classifier Systems and Membrane Computing
    Lecturer or responsible person: Mario Pavone
    Language: Italian
    The course is part of: B.S. in Computer Science
  • Title of the course: Problems and approaches in computational chemistry
    Level: graduate
    Institute and departament: Politecnico di Milano, DEI
    Short description of topics: Computational chemistry is a well developed intersection of chemistry and computer science that employs the results of theoretical chemistry to compute the structures and properties of molecules. Present computational chemistry can accurately calculate the properties of molecules that contain no more than 10-40 electrons. Approximate methods are available for larger molecules. The course will introduce the methods used in the basic areas of computational chemistry: 1. The prediction of the molecular structure of molecules 2. Storing and searching for data on chemical entities 3. Identifying correlation between chemical structures and properties 4. Computational approaches to design molecules that interact in specific ways with other molecules (eg drug design) After a review of the area we will present the open challenges. Challenges in size: work with big molecules (proteins, etc), work on large data sets, etc. Challenges in the meaning: classification of the chemical space, classification of the mechanisms space, etc. Challenges in the perspectives: from "in vivo" testing to "in silico" testing. Challenges in the hybridization with new areas: how proteomics, genetics, neurosciences can build over computational chemistry. The course will be organized with the cooperation of external experts and problem holders.
    Lecturer or responsible person: Giuseppina Gini
    Other people involved: Emilio Benfenati (Mario Negri Institute, Milan)
    Language: English
    Web page: http://home.dei.polimi.it/gini/CompChem/
    Starting year of the course in its present form: 2008
    Goals/contents of the course: The goal is to introduce the students to the topic and to review with them relevant publications in the computer science and AI areas.
    Text book or classnotes: http://home.dei.polimi.it/gini/CompChem/lezioni.htm
    Slides or others supporting material: http://home.dei.polimi.it/gini/CompChem/
    Duration and period: 20 hours
    Approximate number of students: 25
    Intended audience: PhD student in ICT
    The course is part of: Doctorate in Information Technology at Politecnico di Milano
    Type: elective

Mexico    [Top]
  • Title of the course: An Introduction to Evolutionary Computation
    Level: Graduate-level (masters)
    Institute and departament: Department of Computer Science, CINVESTAV-IPN
    Short description of topics: Basics of genetic algorithms (encoding, operators, selection mechanisms, theoretical foundations, parallelism), as well as some notions of evolution strategies, evolutionary programming and genetic programming
    Lecturer or responsible person: Carlos A. Coello Coello
    Language: Spanish
    Web page: http://delta.cs.cinvestav.mx/~ccoello/genetic.html
    Starting year of the course in its present form: 2001
    Goals/contents of the course: To acquire basic knowledge about evolutionary algorithms in general and genetic algorithms in particular (terminology, operators, theoretical foundations).
    Text book or classnotes: http://delta.cs.cinvestav.mx/~ccoello/genetic.html
    Slides or others supporting material: http://delta.cs.cinvestav.mx/~ccoello/genetic.html
    Duration and period: 14 weeks (4 hours a week). Taught in the term May-August of each year
    Approximate number of students: 10
    Intended audience: Graduate students in computer science or a related area
    The course is part of: MSc in Computer Science
    Type: Elective
    Additional information: This course has no prior course requirements, but students need to know C/C++ programming (under Linux) and should have some basic background in math and statistics.
  • Title of the course: Fuzzy Logic and Neural Networks
    Level: Undergraduate
    Institute and departament: CETYS University
    Short description of topics: Basics of softcomputing (fuzzy logic and neural networks) applied to problems of control engineering, pattern recognition, time series prediction and other real problems of implementation.
    Lecturer or responsible person: Nohe R. Cazarez-Castro
    Other people involved: Selene L. Cardenas-Maciel
    Language: Spanish
    Web page: http://www.nohe.mx
    Starting year of the course in its present form: 2000
    Goals/contents of the course: Knowing the theory and applications of soft computing.
    Text book or classnotes: http://www.nohe.mx
    Slides or others supporting material: http://www.nohe.mx
    Duration and period: 16 weeks, 4 hours a week
    Approximate number of students: 10
    Intended audience: Undergraduate students of the Engineering Department
    The course is part of: BS Electronics Systems (optional for other fiels)
    Type: Compulsory for BS Electronics Systems and elective for other fields.
    Additional information: http://www.nohe.mx

Netherlands    [Top]
  • Title of the course: Computational Intelligence
    Level: Graduate
    Institute and departament: Erasmus School of Economics, Erasmus University Rotterdam.
    Short description of topics: This course gives an in-depth introduction to the computational approaches for intelligent systems design. The emphasis is on the background and application of soft computing techniques. In particular, (a selection of) the following topics are considered. - Modelling with fuzzy systems. - Feed-forward neural networks. - Self-organizing maps. - Derivative-free optimisation with evolutionary algorithms. - Neuro-fuzzy systems. - Intelligent agents. The relevance of these techniques is demonstrated by examples of applications from finance, logistics, marketing and economic modelling. Students also gain hands-on experience with Matlab in applying these techniques.
    Lecturer or responsible person: Uzay Kaymak
    Language: English
    Starting year of the course in its present form: 2002
    Goals/contents of the course: At the end of this course the student is able to: - Describe computational approaches to intelligence. - List the reasons for using computational intelligence systems. - Design computational intelligence systems by following a data-driven or an expert-driven approach. - Implement a computational intelligence technique in a programming environment like Matlab. - Apply intelligent systems for solving problems in the domain of economics and management science.
    Duration and period: September - October, 8 weeks
    Approximate number of students: 20
    Intended audience: Master students, junior post-graduate students.
    The course is part of: M.Sc. in Economics & Informatics, Computational Economics
    Type: Compulsory

New Zealand    [Top]
  • Title of the course: Advanced Mechatronics
    Level: undergraduate/graduate
    Institute and departament: Victoria University of Wellington, School of Engineering and Computer Science
    Short description of topics: Application of Computational Intelligence techniques to real-world problems. Application issues, rather than theory, are described as part of an Advanced Mechatronics course. Problem domains ranging from data mining to cognitive robotics are discussed. Topics from modern heuristics to advanced Evolutionary Computation techniques are investigated. The Torcs autonomous racing car platform is used for the course assessment to implement computational intelligence techniques in a complex environment. this course is intended to show the capability of Soft Computing techniques to solve interesting and fun problems.
    Lecturer or responsible person: Dr Will Browne
    Other people involved: Prof Dale Carnegie
    Language: English
    Web page: http://ecs.victoria.ac.nz/
    Starting year of the course in its present form: 2006
    Goals/contents of the course: This course provides a guide to advanced techniques in the field of Mechatronics. Design and construction of computer based systems, including the interaction between hardware, software and communication components focuses on embedded systems. Practical examples are drawn from sensors, measurement instruments, robots and cell phones to demonstrate the nature of these interactions. Artificial Intelligence techniques are introduced as a practical method for addressing these complex interactions.
    Text book or classnotes: Artificial Intelligence, Rob Callan, Palgrave
    Slides or others supporting material: n/a on Blackboard so not externally accessible
    Duration and period: one trimester-12 weeks
    Approximate number of students: 12
    Intended audience: Fourth year undergraduate/Masters students
    The course is part of: BE and ME in Engineering and Computer Science,
    Type: Elective

Philippines    [Top]
  • Title of the course: Computational Intelligence I
    Level: Graduate
    Institute and departament: University of the Philippines, Department of Computer Science
    Short description of topics: Basic Concepts, Genetic Algorithms, Evolutionary Strategies, Evolutionary Programming. Genetic Programming. Ant Colony Systems, Particle Swarm Optimization, Memetic Algorithms, Student Mini-Project
    Lecturer or responsible person: Pros Naval
    Language: English
    Starting year of the course in its present form: 2008
    Duration and period: 1 semester (48 hours)
    Approximate number of students: 15
    The course is part of: MS Computer Science, PhD Computer Science
    Type: Elective

Poland    [Top]
  • Title of the course: Computational Intelligence
    Level: graduate
    Institute and departament: Nicolaus Copernicus University, Dept. of Informatics
    Short description of topics: Computational Intelligence (CI) overview, types of adaptive systems, learning and applications. (2 h) Visualization and exploratory data analysis: few variables, parallel coordinates and other direct multivariate visualization algorithms, Principal Component Analysis (PCA), Self-Organized Mappings (SOM) and Multidimensional Scaling (MDS). (7 h) Theory: overview of statistical approaches to learning, bias-variance decomposition, expectation maximization algorithm, model selection, evaluation of results, ROC curves. (4 h) CI packages in action: WEKA/RapidMiner and GhostMiner, presentation of algorithms available in these packages (2 h, more during lab) Statistical algorithms: discriminant analysis - linear (LDA), Fisher (FDA), regularized (RDA), probabilistic data modeling, SVM and kernel methods. (4 h) Density estimation and rule induction, separability criteria. (4 h) Similarity based methods, generation of prototypes, similarity functions. (2 h) Improving CI models: boosting, stacking, ensemble learning, meta-learning, information theory for selection of features. (5 h)
    Lecturer or responsible person: Wlodzislaw Duch
    Language: English or Polish
    Web page: http://www.is.umk.pl/~duch/Wyklady/CI_plan.html
    Starting year of the course in its present form: 2003
    Goals/contents of the course: Intro to CI, understanding methods in large packages
    Text book or classnotes: http://www.is.umk.pl/~duch/Wyklady/CI_plan.html
    Slides or others supporting material: http://www.is.umk.pl/~duch/Wyklady/CI_plan.html
    Duration and period: 30 h lecturs + 30 h lab
    Approximate number of students: from 5 to 45, depending on the year
    Intended audience: CS and computational physics students
    The course is part of: MSc in computer sciences and in informatics
    Type: elective
    Additional information: Web page has now a header in Polish, but all material is in English. This course was also taught at the School of Computer Engineering, Nanyang Technological University in Singapore, 2003-2007.

Portugal    [Top]
  • Title of the course: Adaptive Business Intelligence (ABI)
    Level: postgraduate
    Institute and departament: University of Minho, Department of Information Systems
    Short description of topics: 1 - Introductory ABI concepts: data mining, prediction, optimization and adaptability. 2 - Modern Learning and Optimization methods for ABI: supervised learning (e.g. neural networks, support vector machine, learning classifier systems), clustering, inductive logic programming, heuristic search (e.g. hill-climbing, tabu-search, evolutionary computation). 3 - Data mining and Forecasting for ABI: 4 - Exploration of ABI tools.
    Lecturer or responsible person: Manuel Filipe Santos
    Other people involved: Paulo Cortez and Rui Camacho (from FEUP)
    Language: English
    Starting year of the course in its present form: 2008
    Duration and period: one semester
    The course is part of: PhD program in Computer Science/ MSc in Information Systems Engineering and Management
    Type: Elective
  • Title of the course: Intelligent Decision and Control
    Level: graduate
    Institute and departament: TU Lisbon, IST, Dep. of Computer Science and Engineeirng
    Short description of topics: fuzzy logic, neural networks, bio-inspired meta-heuristics (EA, GA, ACO, PSO, etc.), nonlinear modeling, classification
    Lecturer or responsible person: João Miguel Sousa
    Language: English/Portuguese
    Web page: https://fenix.ist.utl.pt/disciplinas/cdi/2009-2010/1-semestre
  • Title of the course: Intelligent Systems
    Level: graduate
    Institute and departament: TU Lisbon, IST, Dep. Mechanical Engineering
    Short description of topics: Fuzzy logic, fuzzy control, neural networks, nonlinear modeling, classification
    Lecturer or responsible person: João Miguel Sousa
    Language: English/Portuguese
    Web page: https://fenix.ist.utl.pt/disciplinas/sint/2009-2010/2-semestre
    Starting year of the course in its present form: 1987

Romania    [Top]
  • Title of the course: Artificial Intelligence
    Level: undergraduate
    Institute and departament: Technical University "Gheorghe Asachi" of Iasi, Department of Computer Science and Engineering
    Short description of topics: Introduction to Artificial Intelligence, Search Strategies, Game Playing, Game Theory, Constraint Satisfaction Problems, Evolutionary Algorithms, Knowledge Representation Methods, Inference in Propositional and Predicative Logic, Planning Methods, Fuzzy Logic, Probabilistic Reasoning, Supervised Learning, Classification Techniques, Neural Networks, Unsupervised Learning, Reinforcement Learning, Elements of ALife
    Lecturer or responsible person: Florin Leon
    Language: Romanian
    Web page: http://eureka.cs.tuiasi.ro/~fleon
    Starting year of the course in its present form: 2008
    Goals/contents of the course: The objective of the course is to present an overview of the problems characteristic to artificial intelligence, including search, knowledge representation and planning, with special focus on soft computing techniques such as evolutionary algorithms, fuzzy logic and neural networks.
    Text book or classnotes: http://eureka.cs.tuiasi.ro/~fleon/curs_ia.htm
    Slides or others supporting material: http://eureka.cs.tuiasi.ro/~fleon/curs_ia.htm
    Duration and period: 14 weeks, from October to January
    Approximate number of students: 120
    Intended audience: Undergraduate students with the specializations of "Information Technology" and "Computer Engineering"
    The course is part of: BSc in Computer Science
    Type: compulsory

Russian Federation    [Top]
  • Title of the course: Computational cognition
    Level: undergraduate/graduate/postgraduate
    Institute and departament: Novosibirsk State University, Mathematical Department
    Short description of topics: Main definitions from the Measurement Theory - scales, numeric representations, existence, unicity and adequacy problems; extraction information from data; representation information in the first-order logic; rules discovery on data in the first-order logic that are laws, probabilistic laws and maximum specific laws. Problems of knowledge – statistical ambiguity, prediction from probabilistic rules; new definition of prediction, solution of statistical ambiguity and prediction problems; discovering of the subject domain theory; logic programming, logic programs for expert systems; knowledge extraction from expert; consistent knowledge base including domain theory and expert knowledge.
    Lecturer or responsible person: Evgenii Vityaev
    Language: Russian
    Web page: http://math.nsc.ru/AP/ScientificDiscovery/pages/lectures.html
    Starting year of the course in its present form: 2007
    Goals/contents of the course: Expert system of computational cognition. Full and consistent knowledge extraction from the expert and data.
    Text book or classnotes: http://math.nsc.ru/AP/ScientificDiscovery/pages/BookCC.html
    Slides or others supporting material: http://math.nsc.ru/AP/ScientificDiscovery/pages/lectures.html
    Duration and period: semester, every year
    Approximate number of students: 80-90
    The course is part of: B.S. in Mathematics and Applied Mathematics
    Type: compulsory

Saudi Arabia    [Top]
  • Title of the course: Evolutionary Computation
    Level: Graduate
    Institute and departament: KFUPM, Computer Engineering Department
    Short description of topics: Introduction to the fundamental principles and practices underlying the field of evolutionary computation. Application of evolutionary algorithms to various optimization problems in engineering. Hybridization of evolutionary computing techniques with other disciplines such as Fuzzy logic, Neural Networks etc. Design and Modeling of computer engineering problem solutions based on the principles of evolutionary algorithms.
    Lecturer or responsible person: Zubair Baig
    Language: English
    Web page: http://faculty.kfupm.edu.sa/coe/zbaig/coe589.htm
    Starting year of the course in its present form: 2010
    Duration and period: 1 semester
    Approximate number of students: 10
    The course is part of: MS in Computer Engineering/Science
    Type: Elective

Scotland    [Top]
  • Title of the course: Adaptive Intelligent Systems
    Level: postgraduate masters
    Institute and departament: Robert Gordon University, School of Computing
    Short description of topics: Techniques: evolutionary algorithms (GA, ES, PSO, ACO, EDA), local search, constraint satisfaction and optimisation. Applications: function optimisation, artificial life, network analysis, optimal control, scheduling, evolutionary art and music. Concepts: exploration v exploitation, local and global optima, satisfaction and optimisation, premature convergence, plateauing , linkage-related theory. Practical: problem representations, selection, heuristic operators, parameter choices, evaluation and tuning of algorithms, toolkits
    Lecturer or responsible person: John McCall
    Language: English
    Duration and period: 13 weeks
    Approximate number of students: 20
    Intended audience: Advanced Masters
    The course is part of: MSc Computing: Information Engineering
    Type: Compulsory

Spain    [Top]
  • Title of the course: Algoritmos Bioinspirados
    Level: Graduate/Postgraduate
    Institute and departament: Universidad de Extremadura, Departamento de Tecnología de los Computadores y de las Comunicaciones
    Short description of topics: Evolutionary Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Neural Networks.
    Lecturer or responsible person: Francisco Fernández de Vega
    Language: Spanish
    Starting year of the course in its present form: 2002
    Goals/contents of the course: Introduce the basic concepts of Biologically Inspired Metaheuristics.
    Duration and period: One semester
    Approximate number of students: 15 postgraduates / 5 undergrads
    Intended audience: IT students
    Type: Elective
  • Title of the course: Evolutionary Algorithms
    Level: Postgraduate (master level)
    Institute and departament: University of Málaga, Dpt. Languages and Computing Sciences
    Short description of topics: Introduction to evolutionary algorithms, relation to other metaheuristics (ACO, PSO, DE, SA, SS, TS, VNS...), decentralized algorithms (distributed and cellular EAs), parallel EAs, hybrid and memetic EAs, multiobjective approaches, dynamic optimization, theory, applications (telecoms, bioinformatics, software engineering...)
    Lecturer or responsible person: Prof. Dr. Enrique Alba
    Other people involved: Dr. Carlos Cotta
    Language: Spanish
    Web page: http://mop.cv.uma.es/course/category.php?id=91
    Starting year of the course in its present form: Every year, starting in September
    Goals/contents of the course: Design, implemention and use of evolutionary algorithms (and others) to solve real world problems of high complexity, dimensionality, etc.
    Duration and period: One year, full time
    The course is part of: Master in Software Engineering and Artificial Intelligence
    Type: Elective
  • Title of the course: Evolutionary Computation
    Level: final year undergraduate
    Institute and departament: University Complutense de Madrid. Facultad de Informática
    Short description of topics: Introduction to the fundamental principles and practices underlying the field of evolutionary computation. Application of evolutionary algorithms to various optimization problems
    Lecturer or responsible person: Carlos Cervigón
    Language: Spanish
    Web page: http://www.fdi.ucm.es/profesor/ccervigon/
    Text book or classnotes: Book, Slides and Virtual Carmpus
    Slides or others supporting material: http://www.fdi.ucm.es/profesor/ccervigon/PE/PE.html
    Duration and period: second semester
    Approximate number of students: 60
    Type: elective
  • Title of the course: Fundamentals of Soft computing
    Level: postgraduate
    Institute and departament: Department of Electronics and Computer Science
    Short description of topics: Machine learning, Bayesian networks, Evidence Theory, Fuzzy sets, Fuzzy reasoning
    Lecturer or responsible person: Alberto J. Bugarín Diz
    Other people involved: Paulo Félix Lamas, Miguel Rodríguez González
    Language: Spanish
    Web page: http://www.usc.es/gl/centros/fisica/materia.jsp?materia=38913&ano=60&idioma=7
    Starting year of the course in its present form: 2004
    Goals/contents of the course: The course deals with the foundations of some paradigms that are relevant for the treatment of uncertainty in computing: classical approaches such as the probability theory and the evidence theory and soft computing approaches such as fuzzy sets and the possibility theory. The main goal is to achieve a thorough understanding of the foundations of these paradigms, and also to experiment with their use and practical application in a number of problems, especially in the field of knowledge representation and machine learning.
    Text book or classnotes: http://www.usc.es/gl/centros/fisica/materia.jsp?materia=38913&ano=60&idioma=7
    Slides or others supporting material: http://www.usc.es/campusvirtual/
    Duration and period: 3ECTS; February-May
    Approximate number of students: 5
    Intended audience: Ph.D. students
    The course is part of: M.Sc. "research in information technologies"
    Type: elective
  • Title of the course: Intelligent Control
    Level: Postgraduate
    Institute and departament: University of Huelva, Department of Electronic Engineering, Computer Systems, and Automatics
    Short description of topics: Introduction to intelligent control. Evolutionary computation, neural networks and fuzzy logic from the perspective of the intelligent control.
    Lecturer or responsible person: A. Javier Barragán
    Other people involved: J. Manuel Andújar
    Language: Spanish
    Web page: http://uhu.es/noticieros/master-icseii/
    Starting year of the course in its present form: 2007
    Approximate number of students: 20
    Intended audience: Master students
    The course is part of: Master in engineering control, industrial computer and electronic systems
  • Title of the course: Knowledge Engineering
    Level: undergraduate
    Institute and departament: Department of Electronics and Computer Science
    Short description of topics: -Automatic learning: trees and decision rules. -Knowledge representation and reasoning with uncertainty - Study of practical cases of application
    Lecturer or responsible person: Alberto J. Bugarín Diz
    Language: Spanish; Galician
    Web page: http://www.usc.es/es/centros/etse/materia.jsp?materia=37228&ano=60
    Starting year of the course in its present form: 2009
    Goals/contents of the course: The aim of the subject is to present the student a number of problems for whose resolution is not feasible to apply an algorithm, or whose algorithmic solution turns out to be difficult. These problems often find an acceptable solution through the use of methods involving a representation of the knowledge that is available about the particular problem, or about how humans solve problems in general. Usually this knowledge is endowed with uncertainty. Ways of representing this knowledge on a useful way, on how to automatically acquire the above mentioned knowledge, how to find the most suitable type of reasoning and how to describe processes of reasoning and problem solving are described.
    Text book or classnotes: http://www.usc.es/es/centros/etse/materia.jsp?materia=37228&ano=60
    Slides or others supporting material: http://www.usc.es/campusvirtual/
    Duration and period: 3ECTS; November-January
    Approximate number of students: 40
    Intended audience: CS students
    The course is part of: B.Eng. Computer Science
    Type: Compulsory
  • Title of the course: Master in Soft Computing and Intelligent Data Analysis
    Level: Master
    Institute and departament: European Centre for Soft Computing and Universtity of Oviedo
    Short description of topics: Soft computing, Fuzzy logic, Neural networks, Evolutionary computation, Meta-heuristics, Probabilistic reasoning, Intelligent data analysis, Hybrid systems, ...
    Lecturer or responsible person: Luis Magdalena
    Other people involved: More than forty lecturers including: P. Bonissone, C. Borgelt, J.L. Castro, O. Cordón, S. Crone, D. Dubois, B. Gabrys, M.A. Gil, F. Herrera, J. Kacprzyk, R. Kruse, M. Laguna, P. Larrañaga, R. López de Mántaras R. Martí, C. Moraga, A. Nürnberger, G. Pasi and E. Trillas
    Language: English
    Web page: www.softcomputing.es/master
    Starting year of the course in its present form: 2009
    Goals/contents of the course: The general objective of the Master program is to prepare students for highly qualified positions in a wide range of jobs in the public and the private sector, and to provide students with the foundations required to pursue a PhD degree. Its specific objective is to train researchers to make significant contributions to scientific knowledge in soft computing and intelligent data analysis environment.
    Duration and period: One year, full time
    Additional information: Visit the web http://www.softcomputing.es/master or e-mail your questions to master@softcomputing.es
  • Title of the course: Master in Soft Computing and Intelligent Systems
    Level: Master
    Institute and departament: Department of Computer Science and Artificial Intelligence of the University of Granada
    Short description of topics: Fuzzy Logic, Artificial Neural Networks, Evolutionary Computation and Probabilistic Reasoning, amongst others. Furthermore, it covers the use of these techniques in the context of key problems such as Data Mining, Bioinformatics, the Semantic Web, Robotics and Intelligent Databases, amongst others
    Other people involved: Lectures of the Department of Computer Science and Artificial Intelligence of the University of Granada, along with lecturers from the Universities of Jaen and Cordoba, and prestigious guests from universities throughout the world for the Seminar on New Trends in Soft Computing and Intelligent Systems included as a compulsory course of the master.
    Language: Spanish/English
    Web page: docto-si.ugr.es
    Starting year of the course in its present form: 2006
    Goals/contents of the course: The master in Soft Computing and Intelligent Systems provides advanced training in the field of Intelligent Systems, this training is useful for professional development of students and to provide students with the foundations required to pursue a PhD degree.
    Text book or classnotes: https://docto-si.ugr.es/master/intranet/AuthApp/index.php
    Slides or others supporting material: https://docto-si.ugr.es/master/intranet/AuthApp/index.php
    Duration and period: One year, full time
    Approximate number of students: 35
    Intended audience: This Master is aimed specifically at students with a degree in Computing or Computer Engineering, Electronic, Telecommunications and Industrial Engineering, and those with degrees in Physics or Mathematics or other closely related subjects.
    Type: 2 compulsory subjects and the rest elective
    Additional information: Please visit the website of the graduate school of the University of Granada for administrative matters http://escuelaposgrado.ugr.es/
  • Title of the course: Neural Networks and Fuzzy Logic
    Level: Postgraduate
    Institute and departament: University of Huelva, Department of Electronic Engineering, Computer Systems, and Automatics
    Short description of topics: Introduction to neural networks, perceptrons and backpropagation learning algorithm. Introduction to fuzzy logic and fuzzy systems, fuzzy reasoning, fuzzy modeling and fuzzy control.
    Lecturer or responsible person: A. Javier Barragán
    Other people involved: J. Manuel Andújar, Omar Sánchez
    Language: Spain
    Web page: http://uhu.es/noticieros/master-icseii/
    Starting year of the course in its present form: 2007
    Approximate number of students: 20
    Intended audience: Master students
    The course is part of: Master in engineering control, industrial computer and electronic systems
  • Title of the course: Soft Computing for Scientists
    Level: Postgraduate
    Institute and departament: University of Santiago de Compostela. Department of Electronics and Computer Science
    Short description of topics: Fuzzy logic; Neural computation; Evolutionary computation; Hybrid solutions
    Lecturer or responsible person: Manuel Mucientes
    Other people involved: Manuel Fernández-Delgado, Francisco Herrera
    Language: Spanish
    Web page: http://www.usc.es/gl/centros/fisica/materia.jsp?materia=38901&ano=60&idioma=7
    Starting year of the course in its present form: 2007
    Goals/contents of the course: In this course the student will acquire the basic skills to solve real problems using soft computing techniques. The student will use software tools to apply the theoretical contents of the course to solve classic problems.
    Slides or others supporting material: http://www.usc.es/campusvirtual/
    Duration and period: 3 ECTS; Sep-Jan
    Approximate number of students: 10
    Intended audience: Ph. D. students
    The course is part of: M.Sc. "research in information technologies"
    Type: Compulsory

Switzerland    [Top]
  • Title of the course: Modelling, Simulation and Optimization (Advanced Technologies Supporting Business Areas)
    Level: graduate
    Institute and departament: Institute for Information Systems
    Short description of topics: This module deals with selected methods out of the research and application area of Computational Intelligence. Methods treated in the lectures and seminar projects are, for instance, evolutionary search and optimisation technologies, neural networks, sophisticated data mining technologies, artificial intelligence, and every kind of hybrid intelligent system. Besides the basic foundations and a broader theory, these methods are applied to business issues or other application areas of interest for modelling, simulating and analysing problems, for evaluating and assessing data, as well as for obtaining viable alternatives and optimised solutions. The potential impact of computational intelligence is investigated. Different cases are examined where computational intelligence can provide a substantial support, for instance in management science,operations research, logistics, finance and banking, and computer science. On successful completion of this module, the students will have gained knowledge of the objectives, implementation and use of such methods of computational intelligence.
    Lecturer or responsible person: Rolf Dornberger
    Other people involved: Thomas Hanne
    Language: English
    Web page: http://www.en.fhnw.ch/business/iwi/institute-for-information-systems-where-it-and-business-meet?set_language=en
    Starting year of the course in its present form: 2009
    Goals/contents of the course: 1. Overview of optimization problems Defining, assessing and solving optimization problems Objectives, constraints, parameter sets 2. Application / business areas Examples where computational intelligence is supporting business areas Logistics (airline, railway, etc.), engineering, finance, economics, management 3. Overview of computational intelligence Evolutionary computation (focus), artificial neural networks, fuzzy logic 4. Optimization methods and metaheuristics Genetic algorithm, evolution strategy, simulated annealing, swarm intelligence, ant colony based optimization Software platform for optimization and machine learning Using and extending the software platform Repetition of programming and software engineering: Syntax and usage of Java, object-oriented programming
    Slides or others supporting material: available on request
    Duration and period: 1 semester (15 weeks x 4h)
    Approximate number of students: 20
    Intended audience: Students in the master program in Business Information Systems
    The course is part of: MSc in Business Information Systems
    Type: elective

Taiwan    [Top]
  • Title of the course: Fuzzy Set Theory and Applications
    Level: graduate
    Institute and departament: Industrial Engineering &Engineering Management, National Tsing Hua University
    Short description of topics: Introduction to the theories & logic of fuzzy sets with uncertainties in information and its uses in systems optimization and decision making.
    Lecturer or responsible person: Hsiao-Fan Wang
    Language: English
    Web page: softlab.ie.nthu.edu.tw
    Starting year of the course in its present form: Feb. 2010
    Goals/contents of the course: The main contents are: 1. Introduction and Review of Set Theory 2. Fuzzy Sets and Operations 3. Fuzzy Numbers and Arithmetic 4. Fuzzy Relations 5. Fuzzy Events and Fuzzy Regression 6. Fuzzy Measures 7. Fuzzy Linear Programming 8.*Fuzzy Decision Making 9. Fuzzy Clustering and Pattern Recognition 10Trend of softt Computing
    Slides or others supporting material: softlab.ie.nthu.edu.tw
    Duration and period: One semester
    Approximate number of students: 30
    Intended audience: Engineering and Management students
    The course is part of: master/phd in Industrial Engieering & Engineering Management
    Type: elective
    Additional information: a term project is required

United Kingdom    [Top]
  • Title of the course: Fuzzy Systems and Networks
    Level: final year undergraduate
    Institute and departament: University of Portsmouth, School of Computing
    Short description of topics: Formal models of fuzzy systems and networks, basic and advanced operations in fuzzy networks, feedforward and feedback fuzzy networks, comparative evaluation of fuzzy systems and networks.
    Lecturer or responsible person: Alexander Gegov (course lecturer)
    Other people involved: Nedyalko Petrov (software developer)
    Language: English
    Web page: http://www.port.ac.uk/departments/academic/comp/staff/title,3828,en.html
    Starting year of the course in its present form: 2009
    Goals/contents of the course: To provide students with theoretical knowledge on fuzzy systems and networks as well as with practical experience by using the Matlab Fuzzy Logic and Fuzzy Network toolboxes.
    Text book or classnotes: http://www.springer.com/engineering/book/978-3-540-38883-8
    Slides or others supporting material: http://uws.port.ac.uk/unitwebsearch/displayUnitDetails.do?objectId=58685748
    Duration and period: one semester
    Approximate number of students: 10
    Intended audience: PhD students, researchers, academics.
    The course is part of: BSc in Computer Science
    Type: elective
  • Title of the course: Games Programming Competition
    Level: Undergraduate / Taught Postgraduate
    Institute and departament: College of Engineering, Mathematics and Physical Sciences, University of Exeter
    Short description of topics: Extra-curricular programming course with a 3 hour workshop schedule across two semesters culminating in a programming competition for CI in games agents
    Lecturer or responsible person: Kent McClymont
    Other people involved: Maximillian Dupenois
    Language: English
    Web page: http://people.exeter.ac.uk/km314/index.php?id=cig
    Starting year of the course in its present form: 1
    Goals/contents of the course: To provide a competitive environment with desirable prizes (such as company visits to games companies) to motivate students to explore and develop core programming skills which complement the primary programming modules taught as part of the current degree courses.
    Slides or others supporting material: http://people.exeter.ac.uk/km314/toroidwars2010/
    Duration and period: 15 weeks
    Approximate number of students: 8
    Intended audience: Undergraduates
    The course is part of: No
    Type: Elective
  • Title of the course: Genetic Programming and its Applications
    Level: unergraduate/graduate
    Institute and departament: University of Essex, School of Computer Science and Electronic Engineering
    Short description of topics: The aim of this module is to give an introduction to the main techniques and applications of genetic programming within the broader context of evolutionary computation.
    Lecturer or responsible person: Prof Riccardo Poli
    Language: English
  • Title of the course: MSCI 522 Data Mining
    Level: Postgraduate
    Institute and departament: Lancaster University Management School, Dept. of Management Science
    Short description of topics: Teaching the algorithms of computational intelligence, with prarticular emphasis on neural networks, in the context of corporate data mining.
    Lecturer or responsible person: Dr. Sven F. Crone
    Language: English
    Web page: http://www.lums.lancs.ac.uk/masters/management-science/modules/multivariate-statistics-data-mining/
    Starting year of the course in its present form: 2005
    Duration and period: 20 lecture hours + labs
    Approximate number of students: 40
    Intended audience: Students at Master level
    The course is part of: MSc Management Science, Marketing Analytucs, Logistics & Supply Chain management, Quantitative Finance
    Type: compulsory / elective

United States    [Top]
  • Title of the course: Topics in Intelligent Computing
    Level: graduate/postgraduate
    Institute and departament: University of Texas at El Paso, Department of Computer Science
    Short description of topics: Introduction to advanced concepts and techniques of intelligent and soft computing and their applications. Topics may include neural computations, fuzzy computations, evolutionary computations, intelligent control and intelligent web design. May be repeated for credit when topic varies.
    Language: English
    Web page: http://www.cs.utep.edu/vladik/cs5354.10
    The course is part of: M.Sc. and Ph.D. in Computer Science
    Type: elective
  • Title of the course: Adaptive Optimization
    Level: Graduate
    Institute and departament: Auburn University, Industrial and Systems Engineering
    Short description of topics: Introduction to meta-heuristics, simulated annealing, genetic algorithms, evolutionary strategies, tabu search, ant colony methods, particle swarm optimization, handling constraints, multi-objective optimization
    Lecturer or responsible person: Alice E. Smith
    Language: English
    Starting year of the course in its present form: varies
    Goals/contents of the course: Survey course of popular adaptive optimization methods.
    Duration and period: 1 semester, 3 credit hours
    Approximate number of students: 20
    Intended audience: Graduate students in engineering
    Type: Elective
    Additional information: Extensive programming requirement. Any language acceptable.
  • Title of the course: Bio-Inspired Intelligent Systems
    Level: Undergraduate
    Institute and departament: Murray State University, Engineering and Physics
    Short description of topics: Genetic Algorithms, Particle Swarm Optimization, Neural Networks
    Lecturer or responsible person: James Hefeford
    Language: English
    Starting year of the course in its present form: 2006
    Approximate number of students: 10
    The course is part of: B.S. in Engineering Physics
    Type: elective
  • Title of the course: Fuzzy Set Theory
    Level: Graduate/Undergraduate
    Institute and departament: University of South Florida
    Short description of topics: Fuzzy Sets, fuzzy logic, possibility theory, fuzzy control, fuzzy clustering, fuzzy learning, relation of fuzzy and probability.
    Lecturer or responsible person: Abraham Kandel
    Other people involved: Lawrence Hall
    Language: English
    Starting year of the course in its present form: 1988
    Goals/contents of the course: An overview of fuzzy sets and logic. Application areas are covered, such as control, learning, etc.
    Approximate number of students: 25
    Intended audience: Seniors or MS level students
    Type: Elective
  • Title of the course: Topics in Soft Computing
    Level: undergraduate
    Institute and departament: University of Texas at El Paso, Department of Computer Science
    Short description of topics: Introduction to basic concepts and techniques of soft computing, including neural, fuzzy, evolutionary, and interval computations, and their applications.
    Language: English
    Web page: http://www.cs.utep.edu/vladik/cs5354.10
    Duration and period: 1 semester, taught regularly
    Approximate number of students: 15-20
    The course is part of: B.Sc. in Computer Science
    Type: elective

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