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Data Science Classes for Summer 2018

Summer 2018 is featuring Mathematics graduate online classes
in Data Science

Information on how to register can be found on the University at Albany’s main website at albany.edu or by calling the Department of Mathematics & Statistics at 518-442-4600.

A Mat 590 (2320)
May 29 – June 22 (4 week 1)
Function Theory and Functional Analysis for Applications (3)
This course covers function analytic aspects necessary for applications in various areas of science and engineering, notably in Data Science. Among main topics of the course are:  elementary theory of Lebesgue measure and integration, spaces of Lebesgue integrable functions, Banach spaces and Hanh-Banach theorem, duality in Banach spaces, Fourier transforms and Sobolev spaces, Hilbert spaces, reproducing kernel Hilbert spaces, non-linear analysis in Banach spaces. Course is useful for graduate students in the general area of engineering, especially electrical and computer engineering.  Prerequisites: Basic linear algebra, e.g., AMAT 220; calculus of several variables, e.g., AMAT 214 or by the instructor’s permission.

A Mat 592 (2321)             
June 25 – July 20 (4 week 2)
Machine Learning (3)
The primary goal of this course is to provide students with statistical tools and mathematical principles needed to solve both the traditional and modern data science problems encountered in practice. In particular, the course covers a wide variety of topics in machine learning. It introduces the key terms, concepts and methods in machine learning, with an emphasis on developing critical analytical skills through hands-on exercises of actual data analysis tasks. At the same time, it will cover modern machine learning topics such as boosting and online learning for large-scale data analysis. In addition, the students will practice basic programming skills to use software tools in machine learning. This course includes many specific examples illustrating the practice of Machine Learning and is useful for both graduate students and working professionals wishing to enhance their data analytics skills.  Prerequisites: Linear algebra, e.g., AMAT 220; multivariable calculus, e.g., AMAT 214; basic probability and statistics, e.g., AMAT 554.