The course will provide students with more specialized knowledge and skills for designing quantitative research at the doctoral level, including understanding multivariate data analysis and applying more advanced statistical concepts.
The analysis of genomic data course examines the approach for the analysis and display of large scale biological data sets using various algorithms and machine learning techniques. Topics include clustering techniques for gene expression and protein data analysis, machine learning techniques, biological networks, joint learning from multiple data sources, visualization issues for large scale biological data sets.
The algorithm design course provides the basic concepts and principles to examine and design efficient algorithms for a variety of computational problems and applications. Topics include dynamic programming, methods of algorithm design and analysis including data structures, network flows, matching, and linear programming, ellipsoid algorithm, probabilistic algorithm techniques, approximation algorithms for NP problems, geometric algorithms, number theoretic algorithms, on-line computation, and parallel computing.
The mathematical neurobiology course provides insight to the elements of neurophysiology and neuroanatomy for the development of quantitative models of nerve cell and brain phenomena and to develop and analyze several different mathematical models in neurobiology.
The bioinformatics course provides computational methods and development of algorithms to address problems in molecular biology.
The algorithms for computational biology course provide the background knowledge useful for the design of algorithms for analysis of biological systems.
The theory of computations course provides the fundamental complexity theory and models useful for solving computational problems. Topics include basic computational theory, computational models including nondeterministic alternating and probabilistic machines, Boolean circuits, complexity classes related to models of computing including NP, polynomial hierarchy, BPP among others, complete problems, interactive proof systems and probabilistic proofs, randomized algorithms, structural complexity and complexity hierarchy.
The digital image processing course examines the properties of digital images and the method of processing.
The biomedical signal processing course provides an application-intensive approach to biomedical signal processing and application of mathematical tools.
The computer vision course involves the development of algorithms and software that have the potential to mimic a biological organism's ability to see.