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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.
|
| | 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
|
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 |
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
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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. |
| Neuronal computation |
McCulloch & Pitts model: weightes, threshols, fields, activation function, activation. Artificial neural network architectures. Neuronal models classification. |
| Associative memory and optimization |
Hopfield networks: Hebb rule, dynamics, energy. Application for combinatiorial optimization. |
| 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 |
Linear model: principal component analysis. Self-supervised networks. Hebbian learning. Competitive learning. Self-Organized Maps. Adaptive Resonance Theory. Other algorithms. |
| Evolutionary computation |
Genetic algorithms: chromosome, population, reproduction, recombination, mutation, fitness. Genetic programing. Particle Swarm Optimization. Other algorithms. |
| Methodologies :: Tests |
| |
Competences |
(*) Class hours
|
Hours outside the classroom
|
(**) Total hours |
| Introductory activities |
|
1 |
1.5 |
2.5 |
| Lecture |
|
28 |
43.5 |
71.5 |
| Practicals using information and communication technologies (ICTs) in computer rooms |
|
10 |
15 |
25 |
| ICT practicals |
|
4 |
6 |
10 |
| Personal tuition |
|
1 |
0 |
1 |
| |
| Oral tests |
|
1 |
1.5 |
2.5 |
| |
(*) 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 |
Introduction to the development of the course and its contents |
| Lecture |
Contents exposition and availability of the bibliographic materials |
| Practicals using information and communication technologies (ICTs) in computer rooms |
Exposition of tools for the development of solutions and practical resolution of problems |
| ICT practicals |
Practical exercises to attain experience and consolidate the theoretical knowledge |
| Personal tuition |
Personal tuition |
|
Description |
|
Resolució de dubtes sobre els continguts i els exercicis pràctics. Es realitzarà personalment al despatx del professor, o via correu electrònic. |
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Methodologies |
Competences
|
Description |
Weight |
|
|
|
|
| ICT practicals |
|
Evaluation of practical exercises |
70% |
| Oral tests |
|
Exposition of a method related with the course |
30% |
| Others |
|
|
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| |
|
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, ,
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(*)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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