Unlock the Power of Machine Learning in Neuroscience

Authored by Chuck Easttom, “Machine Learning for Neuroscience: A Systematic Approach” is a comprehensive guide that bridges the gap between machine learning and data mining in the field of neuroscience. This book provides a thorough overview of the necessary concepts, math, and programming skills required to develop reliable working models, making it an invaluable resource for neuroscience researchers and practitioners with limited machine learning background.

A User-Friendly Approach to Machine Learning

The book is designed to be easy to follow, with fully working machine learning code examples and downloadable code, making it an ideal textbook for students studying computational neuroscience. The material is presented in a clear and concise manner, covering all the necessary topics, including:

• Basic linear algebra
• Python programming
• Neuroanatomy and physiology
• Cellular neuroscience
• Neurological disorders
• Computational neuroscience

Applying Machine Learning to Neuroscience

The book’s third section delves into the application of machine learning and data mining to neuroscience, covering key topics such as:

• Artificial neural networks (ANN)
• Clustering
• Anomaly detection

With lab assignments and quizzes, this book is perfect for neuroscience researchers, programmers, and students looking to explore the intersection of machine learning and neuroscience.

Key Features:

• Fully working machine learning code examples with downloadable code
• Lab assignments and quizzes for practical application
• Clear and concise presentation of complex topics
• Ideal for neuroscience researchers, programmers, and students

Published by CRC Press, “Machine Learning for Neuroscience: A Systematic Approach” is available in English and can be identified by the following ISBN numbers: 9781032136721 and 9781000907124.