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Dec 15, 2025
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AI 3300 - Statistical Learning
This course gives an overview of the main concepts, techniques and algorithms that are relevant for the theory and practice of statistical learning. The course covers the fundamental topics of classification, regression and clustering, starting with simple learning models such as linear regression, decision trees and logistic regression, and ending with more advanced models including Support Vector Machines and neural networks. This course is required the BS in AI major.
Requisites: AI 3100 and CS 3610
Credit Hours: 3
Repeat/Retake Information: May be retaken two times excluding withdrawals, but only last course taken counts.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Learning Outcomes: - Students will be able to apply supervised learning (classification and regression) and unsupervised learning (clustering).
- Students will be able to apply simple learning models including linear regression, logical regression, perceptrons and decision trees.
- Students will be able to apply concepts of kernels and data processing techniques including feature selection.
- Students will be able to apply linear discriminant models, support vector machines, and Naïve Bayes.
- Students will be able to apply machine learning models in applications areas, including computer vision and natural language processing.
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