Introduction to Unsupervised Learning
STAT 185
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Course Information
Description
An introductory course in unsupervised learning with an emphasis on dimensionality reduction and clustering. Topics include principal component analysis, nonnegative matrix factorization, and spectral clustering. In this course we will study these techniques and others with a focus on high-dimensional geometry and insights provided by linear algebra. Numerous data example will be included throughout the course.
Recommended Prep
Required courses: STAT110; MATH 21a and 21b or equivalent. Basics of R programming and LaTeX are recommended
School
Faculty of Arts & Sciences
Credits
4
Cross Reg
Available for Harvard Cross Registration
Department
Statistics
Course Component
Lecture
NOTE: This course requires additional sections; you will be prompted to choose secondary components during the Add to Cart process
Subject
Statistics
Grading Basis
FAS Letter Graded
Course Requirements
Prerequisite: STAT 110 AND MATH 21A AND MATH 21B
Exam/Final Deadline
Dec. 11, 2026
General Education
N/A
Quantitative Reasoning with Data
N/A
Divisional Distribution
Science & Engineering & Applied Science
Course Level
For Undergraduate and Graduate Students
