Course Catalog Description

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.

Campus	Fall	Spring	Summer
On Campus	X	X
Web Campus	X	X	X

Instructors

Professor	Email	Office
Prof. Kosrow Dehnad	kdehnad1@stevens.edu

More Information

Course Outcomes

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

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

Course Resources

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)

Required Software

RStudio
R
Anaconda (Jupyter)

Grading

Grading Policies

Grades will be based on

Homework - 40%

Midterm - 15%

Choice of in-class final or a Project - 45%

Lecture Outline

	Topic
Week 1	Examples of supervised learning Regression and introduction to Machine learning terms such as, features, learning rate, epoch, loss function
Week 2	Examples of supervised learning Classification: Linear discriminant analysis, Mahalanobis Distance
Week 3	Introduction to Support Vector Machines: Hard Margin and constrained optimization
Week 4,5	Statistics Review Regression Models and Model Selection Logistic Regression
Week 6	Classification and Tree method
Week 7	Basis Expansions and Regularization
Week 8	Resampling Methods: Cross-Validation and Bootstrap Method
Week 9	Nearest Neighbor Method
Week 10	Random Forest
Week 11	Introduction to Neural Networks
Week 12	Recurrent and Convolutional Network
Week 13	Unsupervised Learning and clustering techniques
Week 14	Review/TBD