Prerequisite: ENEE324 or STAT400, Programming skills in Matlab, C+, or Python. Restriction: permission of Electrical & Computer Engineering Department.
Students taking the course as CMSC498M must have completed CMSC330 and CMSC351 with a minimum grade of C-.
A broad introduction to machine learning and statistical pattern recognition. Topics include: Supervised learning (Bayesian learning and classifier, parametric/non-parametric learning, discriminant functions, support vector machines, neural networks, deep learning networks); Unsupervised learning (clustering, dimensionality reduction, autoencoders). The course will also discuss recent applications of machine learning, such as computer vision, data mining, autonomous navigation, and speech recognition.