Course Description:
Provides systematic introduction to machine learning, covering basic theoretical as well as practical aspects of the use of machine learning methods. Topics include learning theory, learning methods, recent learning models, etc. Application examples include multimedia information retrieval, text recognition, computer vision, etc.Prerequisite:
CS 3358 with a grade of C or higher.
Course Objectives:
· The student will be able to describe learning methodologies such as unsupervised learning, supervised learning etc.
· The student will be able to recognize various learning tasks, such as regression and classification, and choose appropriate learning approaches for the task.
· The student will be able to implement machine learning algorithms such as K-means, Hierarchical Clustering, Linear Regression, Logistic Regression, Bayesian Learning, SVM, etc.
· The student will be able to build up learning systems, train the systems on training datasets, and generate predicted results on testing datasets.
· The student will be able to evaluate the performance of machine learning algorithms in terms of precision, recall, and F-measure.
· The student will be able to analyze a target problem and apply machine learning techniques to it.
Course Notes:
Effective fall 2018. Replaced CS 4378V.
Section Info:
(3-0) 3 hr. lecture, 0 hr. lab