FYI, Stat 391 the stat course that counts as a core CSE course will be offered this spring and has new official prereqs of CSE 312, so you should take 312 by winter if you want to take stat 391 this spring. Also, Stat 391 does apply to the minor for math.
Old prereqs: minimum grade of 2.5 in MATH 126; 2.5 in MATH 308; either CSE 326,
CSE 332, CSE 373, CSE 417, or CSE 421
New prereqs: CSE 312 or (STAT 394 and STAT 395)
Catalog description:
The basic concepts of statistics, machine learning and data science, as
well as their computational aspects. Statistical models, likelihood,
maximum likelihood and Bayesian estimation, regression,
classification, clustering, principal component analysis, model
validation, statistical testing. Practical implementation and
visualization in data analysis. Assumes knowledge of basic
probability, mathematical maturity, and ability to program.
Learning Objectives:
– to be able to express the randomness of the observed data as a generative model
– to understand statistical concepts like likelihood, prior, bias, variance
– to understand the differences between the various data analysis tasks (e.g prediction, parameter estimation, dimension reduction) and to be able to cast a specific application question as a statistical question
– to be able to choose a specific method to solve a statistical question, by taking into account the statistical apropriateness of the method and its computational advantages
– to be able to perform statistical procedures on data, using statistical software, including their own software
– to be able to interpret the output of a statistical procedure, and to obtain measures of randomness of this output