A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semi-supervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course.

This course will cover modern and deep neural networks with a focus on how they can be correctly implemented and applied to a wide range of data types. It will cover the backpropagation algorithm and how it is used and extended for deep feedforward, recurrent and convolutional neural networks. An emphasis will be placed on the implementation, design, testing and training of neural networks. The course will also include an introduction to using a modern neural network framework.

This course provides the graduate student an opportunity to explore an aspect of data science independently and in depth, under the direction of an advisor. The student selects a topic and then works with a faculty member to describe the value of the work and the deliverables.

Hallmarks of AI include systems that exhibit human-like behaviors, and AI systems rely on continuous preparation and deployment of data resources as new tasks emerge. In this course, students develop their conceptual, applied, and critical understanding about (1) a full life cycle of conducting AI research; (2) experimental design and statistical methods to collect and validate data resources and conduct computational experiments in AI system development and deployment; (3) human-centered AI concepts and techniques, (4) best practices for technical writing and presentation about AI, and (5) concepts and methods for responsible, fair, and explainable AI. Based on research review, students write and present an experimental design proposal spanning dataset elicitation and computational experimentation, including description and visualization of the experiment and critical reflection on the benefits, limitations, and implications for AI system development and deployment.

This course provides the student with an opportunity to explore a project-based research experience that advances knowledge in that area. The student selects a research problem, conducts background research, develops the system, analyses the results, and builds a professional document and presentation that disseminates the project. The report must include an in-depth research report on a topic selected by the student and in agreement with the student's adviser. The report must be structured as a conference paper, and must be submitted to a conference selected by the student and his/her adviser.

This course provides the student with an opportunity to complete their capstone project, if extra time if needed after enrollment in SWEN-790. The student continues to work closely with his/her adviser.

The first course in a two-course project experience. Students will need to determine, before they take the course, whether they will complete a thesis or a practical project. Students that choose thesis will work with an advisor (called a sponsor) to complete a scientific research study that results in a complete Master’s Thesis. Thesis project details will be determined by the student and their advisor. Students that choose the practical project will preferably work in a group to develop solutions to problems posed by either internal or external customers (also called sponsors). The size of the group may vary, and while a group is preferred, exceptions may be made to allow for one-person projects. The project may require considerable software development or evolution and maintenance of existing software products, and culminates with the completion and presentation of the first major increment of the project solution. Projects will be solicited by the department prior to students selecting (or being assigned to) the project that they will work on for the capstone. Sponsors may be internal or external to the software engineering department; they may be members of other departments, other colleges, other universities, or they may be industry professionals. The primary requirement for sponsors is that the sponsor be an expert in their field.