Tuesdays & Thursdays, 4:00 - 5:15pm
Location: Herzstein Hall 210
Piazza Course Webpage
Office Hours: Thursday 5:15 - 6:30pm
Office Hours: Wednesdays 4-5pm
Recitation: Wednesdays 5-6pm, Mech Lab 254
Elements of Statistical Learning by Hastie, Tibshirani & Friedman.
Introduction to Statistical Learning by James, Witten, Tibshirani & Hastie.
Statistical Learning with Sparsity by Hastie, Tibshirani & Wainwright.
Midterm Exam 20%
Final Project / Competition 40%
Class Participation 5%
A hard copy of homeworks are due in class or by 5pm to the TAs in Duncan Hall. There will be a deduction of 25% of the grade for homeworks that are not typeset using LaTeX. There will be a deduction of 25% of the grade for each day homeworks are late. Homeworks may be discussed with classmates but must be written and submitted individually.
There will be an (open book / open notes) in-class midterm exam on November 7.
Final Project & Contest:
The final project will be a data analysis contest. The competition will begin on September 19 and can be done in teams of two people. Competition Description: [pdf]
September 8: Add Deadline
October 6: Drop Deadline
Announcements, Assignments & Lectures:
Course DescriptionThis course is an introduction to concepts, methods, and best practices in statistical machine learning. Topics covered include regularized regression, classification, kernels, dimension reduction, clustering, trees, and ensemble learning. Emphasis will be placed on applied data analysis. Students will learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms. Students completing this course should be able to (i) apply basic statistical learning methods to build predictive models or perform exploratory analysis, (ii) properly tune, select and validate statistical learning models, and (iii) interpret their results.
Tentative Lecture Schedule:August 22: Introduction & KNN
August 24: MSE & Least Squares
September 5: Ridge Regression
September 7: Sparse Regression I
September 12: Sparse Regression II
September 14: Model Validation I
September 19: Model Validation II
September 21: GLMs I
September 26: GLMs II
September 28 & Sparse GLMs & Bayes Classifiers
October 3: LDA
October 5: SVMs I
October 12: SVMs II
October 17: Intro to Non-Linear I
October 19: Intro to Non-Linear II
October 24: Dimension Reduction I
October 26: Dimension Reduction II
October 31: Clustering I
November 2: Clustering II
November 7: In-Class Midterm Exam
November 9: Trees & Bagging
November 14: Random Forests
November 16: Boosting
November 21: Boosting & Ensemble Learning
November 28: Competition Presentations
November 30: Competition Presentations