# Machine Learning and Neural Networks

### Course description

**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 classical ones.

**GENERAL OBJETIVES**- 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.

**EVALUATION ACTIVITIES**- Classroom exercises.
- Two collaborative Homeworks.
- Individual exam.

A possible relative weightiness for global marking is 2, 4 and 4..