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 the trendy topic of text mining, that is, drawing inference from text data.
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 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.

Required course materials can be accessed through the following links:
1. Dropbox: https://www.dropbox.com/sh/xzo66kmjufg2a91/AAD8TASzpcMES4x3mqKPKyFoa?dl=0
(for syllabus, readings, slides, datasets, etc.)

2. Rpubs: https://rpubs.com/hktseng
(for R code)

This class is graciously 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. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.