36-708 Statistical Methods for Machine Learning
Instructor: Larry Wasserman
Lecture Time: Tuesday and Thursday 1:30 - 2:50
Lecture Location: POS 152
Office Hour: Tuesdays 12:00 - 1:00 Baker Hall 132G
Office: Baker Hall 132G
TA InformationNic Dalmasso Email: firstname.lastname@example.org Office Hours: Wednesdays 4-5 PH 223B
Boyan Duan Email: email@example.com Office Hours: Thursdays 12-1 Baker Hall 132 Lounge
SyllabusClick here for syllabus
Course DescriptionThis course is an advanced course focusing on the intsersection of Statistics and Machine Learning. The goal is to study modern methods and the underlying theory for those methods. There are two pre-requisites for this course:
36-705 (Intermediate Statistical Theory)
Lecture NotesReview Density Estimation Nonparametric Regression Linear Regression Sparsity Nonparametric Sparsity Linear Classifiers Nonparametric Classifiers Random Forests Clustering Graphical Models Directed Graphical Models Causal Inference Minimax Theory Nonparametric Bayesian Inference Conformal Prediction Differential Privacy Optimal Transport and Wasserstein Distance Two Sample Testing Dimension Reduction Boosting Support Vector Machines Online Learning
AssignmentsAssignments are due on Fridays at 3:00 p.m. Upload your assignment in Canvas.
No late assignments will be accepted. If you need an extension due to illness, email me BEFORE the deadline.
Homework 1 (due Friday Feb 1 3:00. Submit a pdf on Canvas) Homework 2 (due Friday Feb 22 3:00. Submit a pdf on Canvas) Homework 3 (due March 29 3:00. Submit a pdf on Canvas) Homework 4 (due April 19 3:00. Submit a pdf on Canvas)
SolutionsHomework 1 Solutions Homework 2 Solutions Homework 3 Solutions Homework 4 Solutions