FA590 Statistical Learning in Finance
Course Catalog Description
|German Creamerfirstname.lastname@example.org||Babbio Center 637|
- 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
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.
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
Whatever you feel is best. If you need help with any of the background, feel free to reach out with questions.
Grades will be based on a combination of quizzes and assignments.
- Quizzes. There will be two multiple choice online quizzes.
- 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%
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.
|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 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|