MLAS 400 - Supervised learning in Autonomous Systems

Credits: 3

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. Using probabilistic programming languages, this course will illustrate supervised machine learning concepts via data preparation, training data, model evaluation, and operationalization. Common machine learning algorithms for supervised learning such as K-nearest neighbors, logistic regression, decision trees and random forests will be explored and operationalized within an autonomous system.

Repeat for Credit
N

Requisites
Prerequisite: STAT 225 and MLAS 350 or CC 315

Typically Offered
Fall

K-State 8
Empirical and Quantitative Reasoning

Kansas State University

Course Catalog

MLAS 400 - Supervised learning in Autonomous Systems