Concha Bielza (Universidad Politecnica de Madrid), Pedro Larranaga (Universidad Politecnica de Madrid)

Data-Driven Computational Neuroscience (eBook, PDF)

Machine Learning and Statistical Models

• Format: PDF

Produktbeschreibung

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.

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Produktdetails

• Verlag: Cambridge University Press (Virtual Publishing)
• Erscheinungstermin: 11. November 2020
• ISBN-13: 9781108639040
• Artikelnr.: 70911056

Autorenporträt

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.

Inhaltsangabe

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.