Machine Learning in Data Science

Catalog description

Machine learning is a highly interdisciplinary subject that encompasses the techniques from statistics, probability, linear algebra, optimization, and computer science. Machine learning techniques are being used in several areas such as face recognition, self-driving cars, cybersecurity, and also in the areas where decisions are very important without human intervention. This course covers both theory and practical algorithms for machine learning for a variety of applications. We cover topics such as supervised learning (generative/discriminative learning, parametric/nonparametric learning, neural networks, and support vector machines), unsupervised learning (clustering, dimension reduction, kernel methods), learning theory, reinforcement learning, and adaptive control. This course will also discuss a variety of recent applications of machine learning, such as data mining, autonomous navigation, and web data processing.