|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
This course covers recent advances in machine learning and intelligent information retrieval. It focuses on how these topics relate to and are applied in data mining. Prerequisite/Restriction: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science
-
3.00 Credits
This course provides a deep dive into advanced topics in data mining, data analysis, and pattern recognition. The course explores high-dimensional data, graph and temporal data, frequent patterns, and current trends and ethical issues of data mining. Additional work is required for CS 7675. Prerequisites: One of the following: CS 5080/CS 6080 CS 5665 CS 5830/CS 6830 CS 6665 Instructor permission Course Also taught as: CS 7675
-
3.00 Credits
This course provides an introduction to theories and techniques of machine intelligence, with emphasis on image processing, pattern recognition, and computer vision. Additional coursework is required for those enrolled in the graduate-level course. Prerequisites: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science Also Taught As: CS 5680
-
3.00 Credits
This course takes a principled and hands-on approach to deep learning with neural networks, covering machine learning basics, backpropagation, stochastic gradient descent, regularization, and universality. Feedforward, convolutional, generative adversarial, and recurrent networks are discussed, as well as autoencoders, other modern architectures, and training techniques. Additional coursework is required for those enrolled in the graduate-level course Prerequisite(s): MATH 1220; MATH 2270 or instructor permission; STAT 3000 or MATH 5710 Prerequisite Recommendation(s): MATH 2210 and MATH 5710 are recommended. Programming experience, preferably in Python, is also strongly recommended Repeatable for credit: N Grade Mode: Standard
-
3.00 Credits
This course introduces topics in computing education research, including learning science, cognitive science, motivation and affect, statistical methods, qualitative methods, pedagogy, assessment, equity and diversity, programming paradigms, and computing for other disciplines. Additional coursework is required for those enrolled in the graduate-level course. Dual-listed as: CS 5750 Repeatable for credit: No Grade Mode: Standard
-
3.00 Credits
Covers advanced topics in database systems, including XML, OODBMS, query optimization, query processing, deductive databases, concurrency, theory of relational databases, normalization, and recovery. Prerequisite/Restriction: 3.0 GPA; grade of B- or better in CS 5800 and enrollment in Computer Science master's or PhD program.
-
3.00 Credits
This course offers a hands-on, programmatic introduction to the nature and computation of wavelets for the analysis and synthesis of digital image and audio data. The course emphasizes the practical significance of wavelet algorithms in computer vision and audio analysis and encourages provocative questions, discussions, and creative thinking. CS 6810 and CS 7810 are cross listed, but CS 7810 requires additional work. Prerequisites: Enrollment in one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science Crosslisted as CS 7810
-
3.00 Credits
This course introduces design principles and techniques for visualizing data. Students learn how visual representations support analysis and understanding of complex data and how to design effective and interactive visualizations. This is a TEAMWORK course. Additional coursework is required for those enrolled in the graduate-level course. Cross/Dual listed as: CS 5820 Prerequisite(s)/Restriction(s): Enrollment one of the following programs, or instructor permission: MCS (Master of Computer Science) MS in Computer Science MS in Data Science PhD in Computer Science
-
3.00 Credits
In this project-based course, students from multiple disciplines work in teams to analyze data from real-world projects. Project teams group technical students from CS with domain experts from outside of CS to analyze data using state-of-the-art data science tools and techniques. Additional coursework is required for those enrolled in the graduate-level course. Registration Restriction(s): Enrollment in one of the following programs, or instructor permission: Master of Computer Science - MCS, Computer Science - MS, Data Science - MS, Computer Science - PhD Dual-listed as: CS 5830 Repeatable for credit: No Grade Mode: Standard
-
3.00 Credits
This course introduces social network analysis as the study of social ties through the lens of networks and graph theory. It explores essential concepts and techniques to investigate and find meaningful patterns in social networks, e.g., Twitter. Additional coursework is required for those enrolled in the graduate-level course. Registration Restriction(s): Enrollment in one of the following programs, or instructor permission: Computer Science - Master of Computer Science - MCS Computer Science - MS Data Science - MS Computer Science - PhD Dual-listed as: CS 5840 Repeatable for credit: No Grade Mode: Standard
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|