Introduction

This course provides an applied overview of both classical linear approaches to statistical learning and more modern statistical methods with emphasis in application to financial markets. The classical linear approaches will include logistic regression, linear discriminant analysis, k-means clustering, and nearest neighbors.The more modern approaches will include generalized additive models, decision trees, boosting, bagging, support vector machines, and others such as Neural Networks.

Course Outcomes

At the end of this course, students will be able to:

1. Describe the difference between supervised/unsupervised learning and parametric/nonparametric models.
2. List a variety of techniques for each type of model from above.
3. Apply the various techniques to sets of data.
4. Interpret which model seems to fit the data set the most productively.

Textbook

• Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical Learning with Applications in R. Springer, 2014

  This book is available free from the author's web site at " http://www.bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing.pdf

• Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition., Springer (Tenth Printing) 2013

  This book is available free from the author's web site at " http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf

Technology Requirements

Basic computer and web-browsing skills
• NavigatingCanvas

Technology skills necessary for this specific course

Live web conferencing using Zoom
Knowledge of EXCEL, R or Python (or at least willingness to learn)