Dec 06, 2025  
2025-2026 Undergraduate Catalog 
    
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2025-2026 Undergraduate Catalog
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MLAS 390 - Unsupervised Learning in Autonomous Systems

Credits: 3

Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. Using probabilistic programming languages, students will learn how to develop, interpret and deploy unsupervised machine learning models within autonomous systems. Common machine learning algorithms for unsupervised learning will be leveraged: k-means clustering, principal component analysis, non-negative matrix factorization, singular decomposition, and density-based spatial clustering of application with noise.

Repeat for Credit
N

Requisites
Prerequisite: STAT 225 and MLAS 350 or CC 315

Typically Offered
Spring

K-State 8
Empirical and Quantitative Reasoning


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