|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
4.00 Credits
Students explore the field of robotics through the lens of decision-making algorithms. They examine critical aspects of autonomous systems from a machine learning and data science perspective, with emphasis on sensing, high-level objective planning, motion planning, and human interaction. This is a TEAMWORK course. Additional coursework is required for those enrolled in the graduate-level course. Cross/Dual Listed as: CS 5510 Prerequisites/Restrictions: 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
-
4.00 Credits
This course examines the three main areas of AI: data-driven intelligence, natural language processing (NLP), and planning. Students learn models from big data, investigate systems that understand/generate natural language, and study problem-solving models in domains such as robot navigation and symbolic mathematics. Additional coursework is required for those enrolled in the graduate-level course. 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 Cross-listed as: CS 5600.
-
3.00 Credits
This course introduces students to cognitive modeling, learner modeling, intelligent tutoring systems, adaptive educational systems, and natural language processing for automated feedback. Coursework involves using AI tools for prototype development and a course project of the student's own design. 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 5620 Repeatable for credit: No Grade Mode: Standard
-
3.00 Credits
This course introduces students to machine learning and problem solving techniques based on fuzzy logic. 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
-
3.00 Credits
This course is designed for students to acquire foundational knowledge about deep neural networks and their applications across various AI tasks, including language understanding, speech and image recognition, machine translation, planning, as well as other topics. Additional coursework is required for those enrolled in the graduate-level course. Crosslisted as: CS 5640 Prerequisites/Restrictions: 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 program
-
3.00 Credits
This advanced course covers theories and techniques of machine intelligence using neural networks. It emphasizes various neural network paradigms and the types of problems they are best suited to solve. 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 practical and mathematical introduction to machine learning techniques and principles in supervised and unsupervised settings. Students learn to understand machine learning research papers and gain the practical experience to implement machine learning approaches on real data. Prerequisites/Restrictions: Graduate standing or: MATH 1220 STAT 3000 or MATH 5710 (MATH 5710 preferred) MATH 2210 or MATH/STAT 5645/6645 (MATH/STAT 5645/6645 preferred) MATH 2270 or MATH/STAT 5645/6645 (MATH/STAT 5645/6645 preferred) Experience programming in Python, R, or Matlab is essential for success in the course Cross listed as: STAT 6655
-
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. 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 program
-
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
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Cookies Policy |
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2025 AcademyOne, Inc.
|
|
|