This course will teach you to employ the analytical tools of linear algebra and Fourier transform in understanding hidden Markov models. This course will help you understand why one-dimensional signal analysis can be applied directly to the continuous domains of images and audio signals. And will help you to understand the mathematics of Gaussian curves and why they are so helpful in understanding probability distributions. The course will assist you in understanding the mathematical basis of automatic speech recognition. It will deal with how to apply the fundamentals of probability theory to laboratory science, and the mathematical basis for the construction of nonlinear filters, artificial neural networks, and adaptive space-time signal processors. Here we will explore how you can use linear algebra to understand brain function, as well as apply concepts and methods from linear algebra to problems in engineering and business.
The students are able to explain the basics of MATLAB, optimization theory, and some basic optimization problems including linear and nonlinear programming. They are able to apply optimization techniques to design data analysis based optimization solvers. Accordingly, the students are able to investigate the performance of available solvers to automatically find the optimal solution for a given optimization problem. 7211a4ac4a