Description
Machine learning uses interdisciplinary principles of linear algebra, statistics, probability theory, optimization techniques, and computer programming to construct useful patterns of data and make accurate predictions. Machine learning is becoming prominent, with various applications in the field of biomedical engineering. This course will introduce students to the fundamental theories and algorithms for machine learning. Through this course, students will explore various machine learning algorithms (Bayesian classification, linear/nonlinear regression, logistic regression, support vector machines, principal component analysis, and deep learning) and learn how to implement them using Python.