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Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader¿s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge.
This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.
Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader¿s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge.
This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.
Reza Borhani, PhD is a seasoned machine learning consultant and engineer with a depth of experience developing AI solutions for companies in the healthcare and technology sectors. He has developed and taught a wide range of undergraduate-and graduate-level courses on machine learning, deep learning, reinforcement learning, and mathematical optimization at Northwestern University where he also holds an adjunct faculty position.
Soheila Borhani, MD is a physician-scientist with extensive clinical experience in various in-patient and out-patient settings. Her medical research interests lie in the areas of cancer biology, translational oncology, bioinformatics, and the application of artificial intelligence to cancer diagnostics and prognostics. She has served as editor and reviewer for a number of peer-reviewed journals in the field, and has organized short-courses on the topic of AI in medicine.
Aggelos K. Katsaggelos, PhD is the Joseph Cummings Professor in Electrical and Computer Engineering (courtesy Computer Science and Radiology), and the deputy director of the Center for Computational Imaging and Signal Analytics in Medicine and co-lead of the Augmented Intelligence in Medical Imaging Program under the Institute for Augmented Intelligence in Medicine at Feinberg School of Medicine at Northwestern University. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), SPIE, the European Association for Signal Processing (EURASIP), and the Optical Society of America (OSA).
Offers an accessible introduction to deep learning in medicine
Covers artificial intelligence topics with a focus on application and visualization
Includes over 100 end of chapter exercises
Erscheinungsjahr: | 2022 |
---|---|
Fachbereich: | Andere Fachgebiete |
Genre: | Medizin |
Rubrik: | Wissenschaften |
Medium: | Taschenbuch |
Seiten: | 208 |
Inhalt: |
xi
196 S. 33 s/w Illustr. 89 farbige Illustr. 196 p. 122 illus. 89 illus. in color. With online files/update. |
ISBN-13: | 9783031195013 |
ISBN-10: | 3031195019 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: |
Borhani, Reza
Katsaggelos, Aggelos K. Borhani, Soheila |
Auflage: | 1st ed. 2022 |
Hersteller: |
Springer International Publishing
Springer International Publishing AG |
Maße: | 235 x 155 x 11 mm |
Von/Mit: | Reza Borhani (u. a.) |
Erscheinungsdatum: | 19.11.2022 |
Gewicht: | 0,362 kg |
Reza Borhani, PhD is a seasoned machine learning consultant and engineer with a depth of experience developing AI solutions for companies in the healthcare and technology sectors. He has developed and taught a wide range of undergraduate-and graduate-level courses on machine learning, deep learning, reinforcement learning, and mathematical optimization at Northwestern University where he also holds an adjunct faculty position.
Soheila Borhani, MD is a physician-scientist with extensive clinical experience in various in-patient and out-patient settings. Her medical research interests lie in the areas of cancer biology, translational oncology, bioinformatics, and the application of artificial intelligence to cancer diagnostics and prognostics. She has served as editor and reviewer for a number of peer-reviewed journals in the field, and has organized short-courses on the topic of AI in medicine.
Aggelos K. Katsaggelos, PhD is the Joseph Cummings Professor in Electrical and Computer Engineering (courtesy Computer Science and Radiology), and the deputy director of the Center for Computational Imaging and Signal Analytics in Medicine and co-lead of the Augmented Intelligence in Medical Imaging Program under the Institute for Augmented Intelligence in Medicine at Feinberg School of Medicine at Northwestern University. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), SPIE, the European Association for Signal Processing (EURASIP), and the Optical Society of America (OSA).
Offers an accessible introduction to deep learning in medicine
Covers artificial intelligence topics with a focus on application and visualization
Includes over 100 end of chapter exercises
Erscheinungsjahr: | 2022 |
---|---|
Fachbereich: | Andere Fachgebiete |
Genre: | Medizin |
Rubrik: | Wissenschaften |
Medium: | Taschenbuch |
Seiten: | 208 |
Inhalt: |
xi
196 S. 33 s/w Illustr. 89 farbige Illustr. 196 p. 122 illus. 89 illus. in color. With online files/update. |
ISBN-13: | 9783031195013 |
ISBN-10: | 3031195019 |
Sprache: | Englisch |
Ausstattung / Beilage: | Paperback |
Einband: | Kartoniert / Broschiert |
Autor: |
Borhani, Reza
Katsaggelos, Aggelos K. Borhani, Soheila |
Auflage: | 1st ed. 2022 |
Hersteller: |
Springer International Publishing
Springer International Publishing AG |
Maße: | 235 x 155 x 11 mm |
Von/Mit: | Reza Borhani (u. a.) |
Erscheinungsdatum: | 19.11.2022 |
Gewicht: | 0,362 kg |