Objectives and competences

Teaching objectives

1. Promote the use of Artificial Intelligence/pattern recognition tools to solve problems in Engineering;
2. Increasing the interest in the programming area for the intelligent Engineering solutions development.

Expected results
The student should be able to:

1. Know the principles, mechanisms, syntax and semantics of programming in Python®;
2. Recognize the need and the advantages of automatic information processing;
3. Express themselves correctly in an oral and written form about classification and advanced signal/data processing.
4. Use the principles and the main advanced techniques of signal processing (eg. temporal, spectral and multiband analysis) for pattern extraction;
5. Use classification tools for solving signal/data processing problems through the creation of support decision systems.

Teaching Methodologies

Theoretical and practical classes with student’s permanent involvement.

Syllabus

1. Python® Programming Language
   - Python® Language Fundamentals
   - Variables, data types and operations
   - Decision and Loop Structures
   - Functions
   - Object oriented Programming
    
2. Artificial Intelligence (AI)
   - What is it?
   - The AI main paradigms and challenges
    
3. Pattern Recognition Concepts
   - What is machine learning?
   - Learning Problems
   - Overfitting and data store problems
   - Some classic machine learning tools
    
4. Supervised Learning
   - Artificial Neural Networks (ANN)
   - Support Vector Machines (SVM)
    
5. Unsupervised Learning
   - K-means and Self-organizing maps (SOM)
    
6. Theory of Learning and Models/Pattern Selection
   - Cross-validation
   - Genetic Algorithms
   - Pattern sequential selection
   - Statistical Methods
    
7. Advanced Intelligent Analysis in Signal Processing
   - Smart applications in Python®