3.00 Credits
This course provides students with a broad introduction to machine learning, datamining, and statistical patter recognition. Students will study data exploration, decision-tree, K-nearest, neighborhoods, linear regression, logistic regression, support vector machines, neural networks, ensemble learning, clustering, dimensionality reduction evaluations. Students will be required to build predictive models based on machine algorithms. For graduate credit a student will be required to write a term paper or execute a project which reflects deeper investigation of the topics covered in the course.