2. Knowledge Representation |
2.1. Knowledge-based systems.
2.2. Logical representation of knowledge.
2.3. Principles of propositional and first-order logic.
2.4. Inference mechanisms.
2.5. Applications in products and services for biomedical engineering. |
4. Expert Systems |
4.1. Definition and theoretical contextualization.
4.2. Types and components of expert systems.
4.3. Development of expert systems.
4.4. Deterministic models and stochastic models.
4.5. Inferential approaches.
4.6. Applications in products and services for biomedical engineering. |
6. Neural Networks |
6.1. Definition and theoretical contextualization.
6.2. The connectionist paradigm versus the symbolic one.
6.3. Usual types and architectures.
6.4. Training methods.
6.5. Types of learning: supervised, unsupervised, reinforced.
6.6. Applications in products and services for biomedical engineering.
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8. Decision Support Systems |
8.1. Definition and theoretical contextualization.
8.2. Components and development.
8.3. Relationship with intelligent systems. Complementary operation.
8.4. Verification, validation and contrast of results.
8.5. Search for the best hypothesis.
8.6. Applications of biomedical decision systems. |
Assignments
Practical implementation on products and services |
1. Definition of the problem within the biomedical engineering sector.
2. Evaluation of its relevance and integration with an intelligent product or service.
3. Search for solutions in the field of artificial intelligence.
4. Identification of criteria, variables, descriptors and any other relevant information.
5. Proposal of conceptual diagram of solution and evaluation of data flow.
6. Implementation of the solution.
7. Validation of results.
8. Dissemination, communication and presentation of the proposed solution. |