Ulteriori informazioni
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
Sommario
Part I. Introduction; Section 1. Computational Neuroscience; Part II. Statistics; Section 2. Exploratory Data Analysis; Section 3. Probability Theory and Random Variables; Section 4. Probabilistic Interference; Part III. Supervised pattern recognition; Section 5. Performance Evaluation; Section 6. Feature subset selection; Section 7. Non-probabilistic classifiers; Section 8. Probabilistic classifiers; Section 9. Metaclassifiers; Section 10. Multi-dimensional classifiers; Part IV. Unsupervised pattern recognition; Section 11. Non-probabilistic clustering; Section 12. Probabilistic clustering; Part V. Probabilistic graphical models; Section 13. Bayesian networks; Section 14. Markov networks; Part VI. Spatial statistics; Section 15. Spatial statistics.
Info autore
Concha Bielza is a professor in the Department of Artificial Intelligence at Universidad Politécnica de Madrid. She has published more than 120 journal papers and coauthored the book Industrial Applications of Machine Learning (2019). She was awarded the 2014 UPM Research Prize.Pedro Larrañaga is a professor in the Department of Artificial Intelligence at Universidad Politécnica de Madrid. He has published more than 150 journal papers and coauthored the book Industrial Applications of Machine Learning (2019). He is fellow of the European Association for Artificial Intelligence and of Academia Europaea.
Riassunto
Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-depth guide to statistical and machine learning methods.
Testo aggiuntivo
'This book provides us with an outstanding text dealing with the multiple applications in modern neuroscience of statistical and computational models learned from data. There is no doubt that new neuroscience technologies and computational neuroscience methods will make it possible to define the structural and functional design of brain circuits and to determine how these designs contribute to the functional organization of the brain. This book contains numerous examples of the current applications of computational neuroscience in various fields of neuroscience, presented in such a way that it is easily accessible to those who are not experts in the field. Therefore, the book also represents an excellent opportunity for neuroscientists from all fields to be introduced to this fascinating world of computational neuroscience, expertly guided by Concha Bielza and Pedro Larrañaga - two eminent scientists specializing in computer science and artificial intelligence.' Javier DeFelipe, Instituto Cajal and Centro de Tecnología Biomédica
