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OverviewThis 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. Full Product DetailsAuthor: Chuck EasttomPublisher: Taylor & Francis Ltd Imprint: CRC Press Weight: 0.725kg ISBN: 9781032136721ISBN 10: 1032136723 Pages: 290 Publication Date: 31 July 2023 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. 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.ReviewsAuthor InformationDr. 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 master’s 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. Tab Content 6Author Website:Countries AvailableAll regions |
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