Objectives and competences
- Promote the use of Artificial Intelligence/pattern recognition tools to solve problems.
- Increasing the interest in the programming area for the development of intelligent solutions with aim of decision support
Expected results
The student should be able to:
- Know the principles, mechanisms, syntax, and semantics of programming in Python®.
- Recognize the need and the advantages of automatic information processing.
- Express themselves correctly in an oral and written form about classification and advanced signal/data processing.
- Use classification tools for solving big data processing problems through the creation of support decision systems.
Teaching Methodologies
The teaching methodology is based on a constructionist pedagogical model focused on the development of computational thinking, through a set of active pedagogical dynamics, by solving problems that appeal to creativity for the development of low/medium complexity software supported by machine learning approaches. The curricular unit is designed from a gamification perspective, combining a componente supervised by the teacher and another to be developed by the student independently.
Syllabus
- Python® Programming Language
- Python® Language Fundamentals
- Variables, data types and operations
- Decision and Loop Structures
- Functions - Artificial Intelligence (AI)
- What is it?
- The AI main paradigms and challenges - Pattern Recognition Concepts
- What is machine learning?
- Learning Problems
- Overfitting and data store problems
- Some classic machine learning tools - Supervised Learning
- Artificial Neural Networks (ANN)
- Support Vector Machines (SVM) - Unsupervised Learning
- K-means and Self-organizing maps (SOM) - Theory of Learning and Models/Pattern Selection
- Cross-validation
- Genetic Algorithms
- Pattern sequential selection
- Statistical Methods - Advanced Intelligent Analysis in Data Processing
- Smart applications in Python®
