@article{1451695,
      recid = {1451695},
      author = {Borhani, Reza, and Borhani, Soheila, and Katsaggelos,  Aggelos Konstantinos,},
      title = {Fundamentals of machine learning and deep learning in  medicine /},
      pages = {1 online resource :},
      abstract = {This book provides an accessible introduction to the  foundations of machine learning and deep learning in  medicine for medical students, researchers, and  professionals who are not necessarily initiated in advanced  mathematics but yearn for a better understanding of this  disruptive technology and its impact on medicine. Once an  esoteric subject known to few outside of computer science  and engineering departments, today artificial intelligence  (AI) is a widely popular technology used by scholars from  all across the academic universe. In particular, recent  years have seen a great deal of interest in the AI  subfields of machine learning and deep learning from  researchers in medicine and life sciences, evidenced by the  rapid growth in the number of articles published on the  topic in peer-reviewed medical journals over the last  decade. The demand for high-quality educational resources  in this area has never been greater than it is today, and  will only continue to grow at a rapid pace. 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 readers 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.},
      url = {http://library.usi.edu/record/1451695},
      doi = {https://doi.org/10.1007/978-3-031-19502-0},
}