CPTR 435 - Machine Learning

Credits: 3
Provides an introduction to designing software systems that can learn from data. Topics include supervised and unsupervised learning techniques, classification vs. regression, model evaluation, generalization issues (e.g. bias-variance tradeoff, overfitting) and current best practices. Applies machine learning to a variety of data sets and covers popular tools used for machine learning on large data sets.

Grade Mode: Normal (A-F,I,W)
Prerequisite(s): CPTR 230   Data Science Fundamentals

OR

CPTR 276   Data Structures and Algorithms
Schedule Type: Lecture
Term Offering: Fall
College Code: CPS

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