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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 |
Become sufficiently independent to work on research projects and scientific or technological collaborations within their thematic area. |
| | CT3 |
Solve complex problems critically, creatively and innovatively in multidisciplinary contexts. |
| | CT5 |
Communicate complex ideas effectively to all sorts of audiences |
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Type C
|
Code |
Competences Nuclear |
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Type A
|
Code |
Learning outcomes |
| | A1 |
Analyse the problems and their causes from a global focus in the medium and long term.
| | | A7 |
Understand the difficulty in handling real multidimensional data, and know some classical linear techniques.
Know neuronal and evolutionary computing techniques applicable to problems regarding the prediction, classification, optimisation, grouping and display of multidimensional data.
| | | G2 |
Apply the techniques learned in a specific context.
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Type B
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Code |
Learning outcomes |
| | CT1 |
Manage and communicate complex information in foreign language.
| | | CT3 |
Recognise the situation as a problem in a multidisciplinary, research or professional environment, and take an active part in finding a solution.
| | | CT5 |
Produce a persuasive, consistent and precise discourse that can explain complex ideas and effectively interact with the audience.
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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 |
15% |
| Assignments |
|
Avaluació dels exercicis pràctics |
85% |
| Others |
|
|
|
| |
|
Other comments and second exam session |
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| Basic |
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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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