18-696 Special Topics in Signal Processing
12 units, Carnegie Mellon University
Statistical learning refers to modeling and analyzing complex datasets using a variety of statistical tools. It is a recently developed area in statistics and blends with parallel developments in computer science and especially in machine learning. With the advent of evergrowing Big Data problems, professional people with statistical learning skills are in high demand. Due to the complexity and depth of this discipline, we expect to offer two consecutive courses I and II spanning two semesters. This course is designed as an introductory class to statistical learning, with no background in mathematical science required. Topics that will be covered in this course include but are not exclusive to basics in statistical learning, linear regression simple linear regression, multiple linear regression, classification logistic regression, linear discriminant analysis, quadratic discriminant analysis, knearest neighbor, resampling crossvalidation, permutation test, bootstrapping, and linear model selection and regularization subset selection, shrinkage method, dimension reduction, high dimensional statistics. Time allowing, we will cover more topics. The programming language R will be introduced and largely used through the entire class. Homework is expected every week. Some are designed for students to implement the statistical learning algorithms using R. Grading will be based on participation in class, homework, midterm exam, and final exam.
Prerequisites: none listed
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