REQUIRED PREVIOUS SUBJETCS
- General Algebra
- Differential equations
DESCRIPCIÓN GENERAL DE LA ASIGNATURA
The main objective is that the student can apply the most important
techniques for Machine Learning, both the “Classical Techniques” and
those based on “Artificial Neural Networks”, to solve problems using
actual data, some of them based on synthetic data, useful for getting
familiar with the techniques, and some others based on data from
real-word applications. The problems include both supervised learning
problems, as well as unsupervised problems. The student is aimed to
understand the features common to any kind of machine learning
technique, and also to be able to understand the advantages and
drawbacks of every technique in order to solve a particular problem. The
classical techniques are studied as the reference techniques that used
mathematical solutions and with which the new soft-computing techniques
based on Neural Networks are to be compared with. The examples are
solved using Matlab © and the specific toolbox of Statistics and
Neural-Networks. A good motivation for using the techniques based in
Neural Networks is given, by presenting the main features and the
general methodology of such bio-inspired techniques, when compared to
- Ability to apply theoretical concepts and techniques included in the table of contents.
- Ability to solve practical problems with both synthetic data and real word data using Matlab ©.
- Ability to team working.
- Classroom exercises.
- Two collaborative Homeworks.
- Individual exam.
A possible relative weightiness for global marking is 2, 4 and 4..