Machine Learning for Neuroscience 1st Edition

Discover how modern algorithms illuminate the brain with Machine Learning for Neuroscience, 1st Edition by Chuck Easttom. This accessible, authoritative guide introduces essential machine learning principles while directly connecting them to neuroscience problems, making it a must-read for anyone working with neural data.

Clear, engaging explanations walk readers through core topics—statistical learning, supervised and unsupervised methods, feature extraction, dimensionality reduction, and emerging deep learning approaches—framed specifically for neuroscience applications. Practical discussions show how these techniques help decode neural signals, interpret imaging and electrophysiological data, and support advances in brain–computer interfaces and cognitive modeling.

Ideal for graduate students, researchers, clinicians, and data scientists, the book balances theoretical rigor with real-world relevance. You’ll gain the conceptual tools to choose appropriate models, evaluate performance, and translate algorithmic results into meaningful biological insights. With concise examples and illustrative figures, complex ideas become approachable without sacrificing technical depth.

Whether you’re in a university lab, a clinical research center, or an AI-driven startup, this edition provides a dependable roadmap to apply machine learning to brain research. Globally oriented and easy to navigate, it’s suited for readers across North America, Europe, Asia, and beyond who seek to bridge computational methods and neuroscience.

Bring clarity to neural data analysis—add Machine Learning for Neuroscience by Chuck Easttom to your professional library and advance your work with the thoughtful combination of machine learning theory and neuroscience practice. Order your copy today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.