Course Synopses

Prerequisite: Calculus II. Proficiency in Linear Algebra.  Currently not offered.

Probability and Bayesian analysis, Central Limit Theorem, Parametric and Non-Parametric Tests of Significance, Sequence Alignment, Phylogenetic Analysis, Clustering and Pattern Recognition, Virus Dynamics, Monte Carlo Simulations, Neural Networks and Evolutionary Game Theory.

If the twentieth century was the century of physics, the twenty-first will likely be the century of biology. Sequencing technologies and novel lab techniques are making it possible to understand how genetic information is stored, accessed, and used by organisms to regulate body tissues and functions across the three domains of life. Exciting discoveries are being made by applying data mining methods (deep learning) on large genetic and genomic datasets. The availability of good public data has opened novel research areas for biologists, clinicians, physicists, computer scientists, mathematicians, chemists, and engineers, allowing them to collaborate and make new and exciting discoveries. This course is intended for students who are interested in learning the techniques necessary to work in these emerging fields of research. The goal is to introduce students to the ideas in the field and provide them with the methods and tools to analyze both small and large datasets. Students will also learn and use Matlab as a programming tool to solve problems.