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Spring 2026
This course focuses on the core principles of RL. Like statistical learning, a central challenge of RL is to generalize learned capabilities to unseen environments. However, RL faces additional challenges such as exploration-exploitation tradeoff, credit assignment, and distribution mismatch between behavior and target policies. Throughout the course, we will delve into various solutions to these challenges and provide theoretical justifications.
4.00
4.00
3.47
Spring 2026
This course provides an overview of the state of the art in software analysis including static and dynamic analysis techniques and verification and validation. It explores the various ways that the analyses are used to predict software behavior. The applications include inference, symbolic execution, fault localization, model checking, security and performance. The course combines theory with practical implementation and usage. Prerequisites: CS 3240.
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Spring 2026
A graduate student returning from Curricular Practical Training can use this course to claim one credit hour of academic credit after successfully reporting, orally and in writing, a summary of the CPT experience to his/her academic advisor.
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Spring 2026
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
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Spring 2026
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
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Spring 2026
Formal record of student commitment to project research for the Master of Computer Science degree under the guidance of a faculty advisor.
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Spring 2026
For master's students who are teaching assistants.
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Spring 2026
Formal record of student commitment to thesis research for the Master of Science degree under the guidance of a faculty advisor. May be repeated as necessary.
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Spring 2026
For doctoral students who are teaching assistants.
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Spring 2026
Formal record of student commitment to doctoral research under the guidance of a faculty advisor. May be repeated as necessary.
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