Statistical Learning
BST 263
Subject & Catalog Number
Course Information
Description
Statistical learning is a collection of flexible tools and techniques for using data to construct prediction algorithms and perform exploratory analysis. This course will introduce students to the theory and application of methods for supervised learning (classification and regression) and unsupervised learning (dimension reduction and clustering). Students will learn the mathematical foundations underlying the methods, as well as how and when to apply different methods. Topics will include the bias-variance tradeoff, cross-validation, linear regression, logistic regression, KNN, LDA/QDA, variable selection, penalized regression, generalized additive models, CART, random forests, gradient boosting, kernels, SVMs, PCA, and K-means. Homework will involve mathematical and programming exercises, and exams will contain conceptual and mathematical problems. Programming in R will be used throughout the course to provide hands-on training and practical examples.
Prerequisites: BST 260 or BST 210 or BST 220 or BST 232 or PHS 2000A.
Course Note: lab or section times to be announced at first meeting.
Class Notes
THIS CLASS HAS PRIORITY ENROLLMENT
Priority Wave Groups
Wave 1 | SM60-HDS
Wave 2 | BIO SM2 / BIO SM60 / BIO SM1 / CBQG SM2
Wave 3 | Open Enrollment
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Priority Wave Timing
Wave 1 | 1/7/2027 11:00 AM - 1/17/2027 11:59 PM
Wave 2 | 1/18/2027 12:00 AM - 1/20/2027 11:59 PM
Wave 3 | 1/21/2027 12:00 AM Enrollment Deadline (varies by session)
Any student who does not meet the Wave 1 or Wave 2 criteria can add themselves to the waitlist (if enrollment requirements are met) at any time during the enrollment period. At the beginning of each priority wave, students on the waitlist who meet the Wave’s criteria will be automatically enrolled into any remaining seats in the course (pending no time conflicts)
**Cross-Registrants and Non-Degree Students will be enrolled on a space available basis after the enrollment deadline for the course
Available for Harvard Cross Registration
