Description: This course provides a fast-paced introduction to a variety of quantitative methods used in biology and their mathematical underpinnings. While no topic will be covered in depth, the course will provide an overview of several different topics commonly encountered in modern biological research including differential equations and systems of differential equations, a review of basic concepts in linear algebra, an introduction to probability theory, Markov chains, maximum likelihood and Bayesian estimation, measures of statistical confidence, hypothesis testing and model choice, permutation and simulation, and several topics in statistics and machine learning including regression analyses, clustering, and principal component analyses.
Prerequisites: Biology 1A, Biology 1B, a course in statistics such as Data 8, Stat 2 or Stat 20, and two semesters of college level math including calculus such as Math 10A and Math 10B. Undergraduate students engaged in honors research, or other supervised research, are preferred. Previous knowledge of R is not necessary.
Credit Restriction: A deficient grade in INTEGBI 120 may be removed by taking INTEGBI 201.