This course is a 2 credit laboratory experience that allows students to apply the biological concepts covered in Biology 151 and 152 Introductory Biology in laboratory and field settings. Students will develop and practice scientific research skills while exploring the areas of genetics, cell and molecular biology, evolution, and ecology. To enroll, students must be co-enrolled in Biology 152 (Introductory Biology II) or have completed the 2 semester Introductory Biology Sequence (Biology 151 and 152).

The theory of sums of squares (SOS) blends exciting ideas from optimization, real algebraic geometry and convex geometry. Indeed, Hilbert's famous characterization of nonnegative polynomials that are SOS in 1888, and Artin's affirmative answer to Hilbert's 17th problem on whether all nonnegative polynomials are SOS of rational functions are at the origins of this topic.

This course is designed to be highly integrative in that students use knowledge and tools from all functional areas of business to develop a "whole organization" perspective. In addition, they integrate by examining these functions within the context of business and societal environments, such as the competitive, political/legal, socio-cultural, technological, and economic. Furthermore, they integrate and further develop general education knowledge and skills, such as written and oral communications in each course assignment.

This course is designed to be highly integrative in that students use knowledge and tools from all functional areas of business to develop a "whole organization" perspective. In addition, they integrate by examining these functions within the context of business and societal environments, such as the competitive, political/legal, socio-cultural, technological, and economic. Furthermore, they integrate and further develop general education knowledge and skills, such as written and oral communications in each course assignment.

Cross-listed with CompSci 575. A basic introduction to combinatorics and graph theory for advanced students in computer science, mathematics, and related fields. Topics include elements of graph theory, Euler and Hamiltonian circuits, graph coloring, matching, basic counting methods, generating functions, recurrences, inclusion-exclusion, Polya's theory of counting. Prerequisites: mathematical maturity, calculus, linear algebra, discrete mathematics course such as CompSci 250 or Math 455. Math 411 recommended but not required.

Distribution and inference for binomial and multinomial variables with contingency tables, generalized linear models, logistic regression for binary responses, logit models for multiple response categories, loglinear models, inference for matched-pairs and correlated clustered data. Prerequisites: Previous course work in probability and mathematical statistics including knowledge of distribution theory, estimation, confidence intervals, hypothesis testing and multiple linear regression; e.g. Stat 516 and Stat 525 (or equivalent). Prior programming experience.

First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. Prerequisites: MATH 132, or 136. (Gen.Ed. R2)

Basic concepts (over real or complex numbers): vector spaces, basis, dimension, linear transformations and matrices, change of basis, similarity. Study of a single linear operator: minimal and characteristic polynomial, eigenvalues, invariant subspaces, triangular form, Cayley-Hamilton theorem. Inner product spaces and special types of linear operators (over real or complex fields): orthogonal, unitary, self-adjoint, hermitian. Diagonalization of symmetric matrices, applications.

Theory and applications of game theory, a major tool of analysis in economics, biology, and political science. Applications include: bargaining, auctions, the "prisoner's dilemma," the "tragedy of the commons," tacit collusion, competition among firms, and strategic interactions in labor, credit, and product markets. Prerequisites: ECON 103 or RES-ECON 102 and MATH 127 or 131 or 135.

Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building.