Fundamentals of Machine Learning and Deep Learning in Medicine

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).

• Introduction.- Mathematical Modeling of Medical Data.- Linear Learning.- Nonlinear Learning.- Multi-Layer Perceptrons.- Convolutional Neural Networks.- Recurrent Neural Networks.- Autoencoders.- Generative Adversarial Networks.- Reinforcement Learning.