Statistical learning is the process of extracting regularities from data using statistical models with the goal of finding a predictive function based on existing data to be able to make prediction on unseen data of similar type. The course introduces students to the concepts and analytical tools of statistical learning, it emphasizes "learning by doing'' by getting students familiarized with the use of R programming language to perform analysis on empirical data. The first part of the course starts with a refresher on the fundamentals of statistics-mean, variance, distribution, probabilities-before proceeding to more specialized topics. The first part of this course also gives a gentle introduction to R programming, during which issues of dimensionality and balance will be discussed with their diagnostic and preprocessing tasks implemented in R. The second part of the course introduces families of binary, penalized, discriminant, and mixture models, along with performance evaluation metrics. We conclude, in the third part of the course, with emerging data analysis methods such as text mining and network analysis.

Each class meeting usually begins with a lecture on that week's topic. During the lecture, the instructor will instruct students how to perform the analytical tasks by running R on the screen. Lecture note and code will be displayed on class slides and available for download. A total of FOUR course assignments will be given throughout the semester, which will help build the necessary analytical and programming foundation toward the completion of a 10-page term project.

This class is supported by Datacamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises.

You also access all course materials via our shared Dropbox folder: https://www.dropbox.com/scl/fo/ypahd0f6wimg0egu3lrhv/h?dl=0&rlkey=31l1qe03rmjflpcqcgdnrmavy