James Jeffry Howbert
Examines current topics and issues associated with computing and software systems. Offered: AWSpS.
Introduction to Machine Learning
Machine learning is the science of building predictive models from available data, in order to predict the behavior of new, previously unseen data. It lies at the intersection of modern statistics and computer science, and is widely and successfully used in medicine, image recognition, finance, e-commerce, textual analysis, and many areas of scientific research, especially computational biology. This course is an introduction to the theory and practical use of the most commonly used machine learning techniques, including decision trees, logistic regression, discriminant analysis, neural networks, na´ve Bayes, k-nearest neighbor, support vector machines, collaborative filtering, clustering, and ensembles. The coursework will emphasize hands-on experience applying specific techniques to real-world datasets, combined with several programming projects.
This course is open to both undergraduate and graduate students and both groups are encouraged to enroll. There will be additional assignments and project deliverables for graduate students.
Student learning goals
General method of instruction
Prerequisites include CSS 342, calculus, and statistics; some prior exposure to probability and linear algebra is recommended.
Class assignments and grading