Search Results

This course discusses the fundamentals and algorithms of machine learning and contains both theory and application. Machine learning, as a subset of artificial intelligence, is the scientific study of models that computer systems use to perform a specific task without explicit instructions. Topics in this course will include supervised learning such as Decision Tree, Perceptron, Support Vector Machine, Naive Bayes, and Regression, unsupervised learning such as clustering, dimensionality reduction, kernel methods, learning theory such as bias/variance trade-offs, Generalization and Overfitting and large margins. Other crucial topics will include discussions such as Stacking, Semi-Supervised Learning and Interactive Learning. This course will also discuss a few applications in problem domains such as in computer vision.

Prerequisite(s): CSCI 2410 or instructor permission. Not open to non-degree graduate students.