K17: Is familiar with microscopy techniques, artificial intelligence and computational models applied to neuroscience. S1: Uses a variety of techniques to find, manage, integrate and critically evaluate available information for the development of professional activities in Neuroscience, especially in the digital sphere S4: Uses their ability to analyse and synthesise, as well as to apply the principles of the scientific method in the work environment, in order to provide innovative responses to the needs and demands of society in their area. S7: Comprehends the computational and experimental tools used for analysis and quantification of neuroscience data, and can appropriately apply these tools to significant problems in neuroscience. C2: Apply knowledge about the organisation, structure and function of the Central Nervous System (CNS) to contribute to the evolution and improvement of technologies and systems for computing, data handling and analysis. C3: Apply knowledge about technologies for the study of the Nervous System and the brain (Medical Imaging, brain-machine interfaces) to develop new systems for diagnosis and treatment, as well as and other applications within Neuroscience (Artificial Intelligence, Robotics) with the aims of improving the quality of life and furthering social progress. C4: Uses advanced mathematical, statistical and computational tools to increase and improve knowledge in neuroscience and its applications. C5: Apply your neuroscience knowledge in a unifying and integrated fashion as part of a multidisciplinary team (pharmaceutical sector, health industry, diagnostic techniques, health information technologies, government agencies and regulatory bodies. C7: Apply the scientific and technical principles you acquired during your undergraduate training, together with your own natural learning capabilities, to better adapt to novel opportunities arising from scientific and technological development.

1. The concept of machine lear.ng 2. Sequence of processes in the implementation of Machine Learning. 3. Selection of the Machine Learning algorithm according to the problem. 4. Python and machine learning. 5. Artificial neural networks. 6. Network topology. 7. Backpropagation. 8. Deep Learning. 9. Application examples. 10. k-Nearest Neighbours (kNN) Algorithm. 11. Distances between data. 12. Selection of a suitable k. 13. Data preparation. 14. Examples of application. 15. Classifier performance measures. 16. Confusion matrix. Associated measures. 17. ROC curves. 18. Sampling techniques for model performance assessment. 19. Classification using Naive Bayes. 20. The Naive Bayes Algorithm. 21. Application examples. 22. Classification with Support Vector Machines (SVM). 23. Maximum margin hyperplane. 24. The use of kernel functions in non-linear problems. 25. Application examples. 26. Decision trees. 27. Decision tree pruning. 28. Application examples. 29. Random Forests. 30. Application examples. 31. Open application of Machine Learning to neuroscience problems

Classroom lectures. Face-to-face classes: reduced (workshops, seminars, case studies). Student individual work. Laboratory session. Final exam. Seminars and lectures supported by computer and audiovisual aids. Practical learning based on cases and problems, and exercise resolution. Individual and group or cooperative work with the option of oral or written presentation. Individual and group tutorials to resolve doubts and queries about the subject. Internships and directed laboratory activities.