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Teaching Methodologies and Assessment Criteria |
Assessment during the teaching-learning period consists of the development of a project, and throughout this development students will be assessed based on 1) deliverables, E (25%), 2) mini-assessment sheets, FA (20%) and 3) demonstrations, D (55%).
1) Deliverables (through documents):
- E1 (individual) - 15%
- E2 (group) - 10%
2) Mini-evaluation sheets (short sheets about theoretical concepts)*:
- FA1 (individual) - 10%
- FA2 (individual) - 10%
3) Demonstrations (short demonstrations of project progress carried out in groups)*:
- D1 - 5%
- D2 - 10%
- D3 - 10%
- D4 - 10%
- D5 (final demonstration) - 20%
Minimum attendance in theoretical-practical classes of 60%*
*Students in special arrangements, or with justified absences, should contact the teacher.
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Main Bibliography |
Russell Stuart & Norvig Peter, "Artificial Intelligence, A Modern Approach", Pearson Series in Artificial Intelligence, Pearson Education Inc., 2019.
Subana Shanmuganathan (Ed), "Artificial Neural Network Modelling", Studies in Computational Intelligence, Springer, 2016.
C Naga Bhaskar & G Vijay Kumar, "Neural Networks and Fuzzy Logic", BS Publications, 2015.
S. Rajasekaran & G.A. Vijayalakshmi Pai, "Neural Networks, Fuzzy Systems and Evolutionary Algorithms, Synthesis and Applications", PHI Learning, 2017.
Oliver Kramer, "Genetic Algorithm Essentials", Studies in Computational Intelligence, Springer, 2017.
Tao Song (Ed), Pan Zheng (Ed), Mou Ling Dennis Wong (Ed), "Bio-Inspired Computing Models and Algorithms", WSPC, 2019.
Maoguo Gong (Ed), Pan Linqiang (Ed), Song Tao (Ed), Ke Tang (Ed), Xingyi Zhang"Bio-Inspired Computing, Theories and Applications" 10th International Conference, BIC-TA 2015 Hefei, China, Proceedings, Springer, 2015.
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