This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience.
This book addresses the growing need for machine learning and data mining in neuroscience. The book is replete with fully working machine learning code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook.
1. Basic Linear Algebra.
2. Overview of Statistics.
3. Introduction to
Python Programming.
4. More with Python.
5. General Neuroanatomy and
physiology.
6. Cellular neuroscience.
7. Neurological disorders.
8.
Introduction to Computational Neuroscience.
9. Overview of machine
learning.
10. Artificial Neural Networks.
11. More with ANN.
12. K Means
Clustering.
13. K Nearest Neighbors.
14. Self Organizing Maps.
Dr. Chuck Easttom is the author of 32 books. He is an inventor with 22 computer science patents. He holds a Doctor of Science in cybersecurity, a Ph.D. in Nanotechnology, and a Ph.D. in computer science as well as three masters degrees (one in applied computer science, one in education, and one in systems engineering). He is a senior member of both the IEEE and the ACM. He is also a Distinguished Speaker of the ACM and a Distinguished Visitor of the IEEE. He has been active in the IEEE Brain Computer Interface Standards and is a member of the IEEE Engineering in Medicine and Biology Society.
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