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Type A
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Code |
Competences Specific | | | A1 |
Integrate the fundamental technology, applications, services and systems of Computer Security and Artificial Intelligence,in a broader, multidisciplinary context.
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| | A7 |
Understand and apply advanced knowledge of high performance computing and numerical or computational problems related to artificial intelligence neural networks and evolutionary systems methods.
|
| | G2 |
Perform mathematical modelling, calculation and simulation in company technology and engineering centres, particularly in tasks of research, development and innovation in the areas of Computer Security and Artificial Intelligence
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Type B
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Code |
Competences Transversal | | | CT1 |
Gestionar i comunicar informació complexa, de temes diversos, amb naturalitat, en llengua estrangera. |
| | CT3 |
Resoldre problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars. |
| | CT5 |
Comunicar idees complexes de manera efectiva a tot tipus d’audiències. |
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Type C
|
Code |
Competences Nuclear |
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Type A
|
Code |
Learning outcomes |
| | A1 |
Analitza els problemes i les seves causes des d'un enfocament global i de mitjà i llarg termini.
| | | A7 |
Comprèn la dificultat en el tractament de dades reals multidimensionals, i coneix algunes tècniques clàssiques lineals.
Coneix tècniques de computació neuronal i evolutiva aplicables a problemes de predicció, classificació, optimització, agrupació i visualització de dades multidimensionals.
| | | G2 |
Aplica les tècniques apreses en contextos concrets.
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Type B
|
Code |
Learning outcomes |
| | CT1 |
Gestiona i comunica informació complexa, de temes diversos, amb naturalitat, en llengua estrangera.
| | | CT3 |
Resol problemes complexes de manera crítica, creativa i innovadora en contextos multidisciplinars.
| | | CT5 |
Comunica idees complexes de manera efectiva a tot tipus d’audiències.
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Type C
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Code |
Learning outcomes |
| Topic |
Sub-topic |
| Multidimensional data |
Basic problems: prediction, classification, optimization, clustering, visualization. Data types: discrete, real, cathegorical. Data preprocessing: outliers, missing data, scaling.
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| Neural computation |
McCulloch & Pitts model: weightes, threshols, fields, activation function, activation. Artificial neural network architectures. Neuronal models classification.
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| Associative memory and optimization |
Associative memory and optimization Hopfield networks: Hebb rule, dynamics, energy. Application for combinatiorial optimization.
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| Supervised learning |
Linear model: multilinear regression. Simple perceptron. Linear networks. Linear separability. Multilayer networks. Back-propagation. Variants of Back-propagation. Cascade Correlation. Support Vector Machines. Other algorithms. |
| Unsupervised learning |
Unsupervised learning Linear model: principal component analysis. Self-supervised networks. Hebbian learning. Competitive learning. Self-Organized Maps. Adaptive Resonance Theory. Other algorithms.
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| Evolutionary computation |
Genetic algorithms: chromosome, population, reproduction, recombination, mutation, fitness. Genetic programing. Particle Swarm Optimization. Other algorithms.
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| Methodologies :: Tests |
| |
Competences |
(*) Class hours
|
Hours outside the classroom
|
(**) Total hours |
| Introductory activities |
|
1 |
1.5 |
2.5 |
| Presentations / oral communications |
|
1 |
1.5 |
2.5 |
| Reading written documents and graphs |
|
28 |
43.5 |
71.5 |
| Assignments |
|
13 |
19.5 |
32.5 |
| Forums of debate |
|
1 |
1.5 |
2.5 |
| Personal attention |
|
1 |
0 |
1 |
| |
| |
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies
| |
Description |
| Introductory activities |
Introducció al desenvolupament de l'assignatura i als seus continguts |
| Presentations / oral communications |
Exposició d'un tema relacionat amb l'assignatura |
| Reading written documents and graphs |
Treball de l'alumne amb materials en format electrònic de l'assignatura i supervisió per part del professor. |
| Assignments |
|
| Forums of debate |
|
| Personal attention |
Atenció personalitzada per vies telemàtiques |
|
Description |
|
Atenció personalitzada per vies telemàtiques |
|
Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
| Presentations / oral communications |
|
Exposició d'un tema relacionat amb l'assignatura |
30% |
| Assignments |
|
Avaluació dels exercicis pràctics |
70% |
| Others |
|
|
|
| |
|
Other comments and second exam session |
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| Basic |
|
Hilera, J.R., Martínez, V.J., Redes Neuronales Artificiales. Fundamentos, Modelos y Aplicaciones. , RA-MA, 1995 Hertz, J.A., Krogh, A., Palmer, R.G., Introduction to the Theory of Neural Computation, Addison-Wesley, 1991
Goldberg, D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989
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| Complementary |
|
Bishop, C.M, Neural Networks for Pattern Recognition, Oxford University Press, 1995 Davis, L. (ed), Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991 Cristianini, N.; Shawe-Taylor, J., An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000 http://www.faqs.org/faqs/ai-faq/neural-nets/, Neural Network FAQ, , http://www.faqs.org/faqs/ai-faq/genetic/, Evolutionary Computation FAQ, , |
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation. |
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