FE590 Statistical Learning in Finance

Course Catalog Description


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


Professor Email Office
Thomas Lonon
tlonon@stevens.edu Virtual

More Information

Course Description


  • This course provides an applied overview of both classical linear approaches to statistical learning and more modern statistical methods.
  • 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.

Prerequisite: Knowledge of R (or willingness to learn) Prob/Stat background

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.

Course Resources


Additional References

Whatever you feel is best. If you need help with any of the background, feel free to reach out with questions.


Grading Policies

Grades will be based on a combination of quizzes and assignments.

  1. Quizzes. There will be two multiple choice online quizzes.
  2. Assignments. There will be four homework assignments in which students will write programs to solve problems related to statistical learning.


In computing the course grade, each activity will be assigned a weight (subject to the instructor's discretion) as follows:

Quizzes - 20%

Assignment 1 - 5%

Assignment 2 - 25%

Assignment 3 - 25%

Assignment 4 - 25%

Extra Credit:

There are no "extra assignments" that students can do to raise their average outside of the ones assigned. There are no exceptions, don't even bother coming to me and asking about extra work and the end of the semester, as I will only direct your attention to this part of the syllabus.

Lecture Outline

Topic Reading
Week 1 Statistics Review
Week 2 Overview of Supervised Learning
Week 3 Linear Methods for Regression
Week 4 Model Assessment and Selection
Week 5 Linear Methods for Regression
Week 6 Classification
Week 7 Basis Expansions and Regularization
Week 8 Model Inference and Averaging
Week 9 Additive Models, Tree Related Methods
Week 10 Improving Trees
Week 11 Neural Networks
Week 12 Support Vector Machines
Week 13 Unsupervised Learning
Week 14 Review and Catch-up