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.