Mathematics for Data Science

This course presents the mathematics of data science methods to promote effective and efficient application as well as innovation in the field. Topics include the bias-variance trade-off, singular value decomposition, principal component analysis and its application to Google's page rank algorithm, gradient descent, support vector machines, kernels, and neural networks. Additional topics may include metric spaces and K-nearest neighbors, information theory. A programming language such as Python, together with relevant Data Science libraries, like TensorFlow, will be used.