An overview of computer science education for undergraduate students. Topics include ethics, diversity, tutoring and teaching techniques, and classroom management. Students enrolled in this course serve as a teaching assistant for a computer science course as part of their coursework.

This is a core Cyber Physical Systems (CPS) class. It provides fundamental core material in signal processing, machine learning, and feedback control. However, the material is not presented in a traditional manner and does not replace deep domain expertise in these topics. Rather, the principles and skills taught in this class highlight the intersection of the cyber and the physical.

Content varies annually, depending on students' needs and interests. Recent topics included the foundations of computation, artificial intelligence, database design, real-time systems, Internet engineering, and electronic design automation. Prerequisite: Instructor permission.

Interactions between robots and humans are influenced by form, function and expectations. Quantitative techniques evaluate performance of specific tasks and functions. Qualitative techniques are used to evaluate the interaction and to understand expectations and perceptions of the human side of the interaction. Students use humanoid robots to develop and evaluate interactions within a specific application context.

In-depth study of a computer science or computer engineering problem by an individual student in close consultation with departmental faculty.

Provides an overview of modern microprocessor design. The topics covered in the course will include the design of super-scalar processors and their memory systems, and the fundamentals of multi-core processor design. Prerequisite: CS 3330 with a grade of C- or better

This course explores Natural Language Processing (NLP), examining how computers are trained to understand and process human language. Students will gain a thorough understanding of both core NLP concepts and advanced techniques, including text analysis, language modeling, machine translation, question answering, text generation, conversation modeling, and the latest advancements in large language models. Prerequisite: CS 3100 with a grade of a C- or better and APMA 3080 or equivalent.

This course aims to give an introduction to basic ideas and concepts of RL while avoiding deep dive in mathematical treatments. The course will help the student build an intuitive understanding of RL and give students hands-on experience on small-scale RL problems. Prerequisite: CS 2100 with a grade of C- or better or successfully complete the CS 2100 place out test.

This course is a general introduction to cryptocurrencies and blockchain applications. Students will understand the theoretical concepts that underlay cryptocurrencies, and implement both their own cryptocurrencies as well as develop applications that run on existing cryptocurrencies. Students will see the ethics, legal, and policy aspects pertaining to the subject. Prerequisite: CS 3100 with a grade of C- or better

Supports the writing of the technical report component of the fourth-year thesis, credit for which is given in STS 4600. Students will write the report assuming a non-technical audience. The course is part of the CS 4XXX elective option in the fourth-year CS thesis track. Student must be a 4th Year BS Computer Science (First or Second Major) and must have completed or be currently enrolled in STS 4500