Statistical learning methods for biological applications including the topics on generative models for count data, clustering, dimension reduction, hypothesis testing, classification and regression, experimental design and software tools in R to visualize and analyze biological data. or MATH 142, or equivalent; STAT 201 or MATH 148, or equivalents Credits 3. 3 Lecture Hours.
Statistical learning methods for biological applications including the topics on generative models for count data, clustering, dimension reduction, hypothesis testing, classification and regression, experimental design and software tools in R to visualize and analyze biological data. or MATH 142, or equivalent; STAT 201 or MATH 148, or equivalents Credits 3. 3 Lecture Hours.
