Program Educational Goals:
The BS in Computer Science draws its foundation from a number of disciplines, requiring students to utilize concepts from different fields. At the core of the curriculum, students learn to integrate computer science theory with practice. In a field that evolves as rapidly as computer science, the program focuses on preparing students for long-term learning that enables them to not only understand today’s technologies, but also understand how to tackle challenges of the future. Students in the program choose a concentration or focus area, which allows a deep exploration of a particular sub-discipline or application domain.
Graduates of the Bachelor of Science in Computer Science program, with concentration in Artificial Intelligence and Robotics, will be able to:
- Design computational solutions to real-world problems, and encode these using a variety of programming languages and paradigms
- Apply theoretical foundations of computing, including automata theory and complexity theory and call upon such knowledge to design efficient solutions
- Design effective assembly language and systems-level programs, applying knowledge of the basic organization of computing hardware as appropriate
- Recognize and employ a range of standard algorithms, data structures, and design patterns, and weigh their advantages and disadvantages to design efficient solutions
- Employ modern software development processes, which include eliciting, analyzing, and specifying requirements, design specification, testing, and verification
- Work effectively on multidisciplinary teams to solve complex problems
- Use knowledge of the social, legal, ethical, and cultural issues inherent in the discipline of computing to guide decisions in real-world situations
- Effectively communicate technical information to a broad audience
- Apply abstract notions of intelligence (rationally choosing actions) including search, logical reasoning, knowledge representation, planning, and reasoning under uncertainty, to problems that do not have obvious efficient algorithmic solutions
- Use a spectrum of methods for pattern analysis and machine learning, including statistical learning approaches, supervised learning, ensemble learning, unsupervised learning, reinforcement learning, and neural networks/deep learning methods
- Implement algorithmic approaches to visual perception such as feature detection, segmentation, 3-D reconstruction and motion tracking.
- The College of Engineering requires nine additional breadth credits (21 credits total including the University Breadth requirements) (minimum grade of C-).
- These nine credits may be selected in any combination from the University Breadth Requirements list and the College of Engineering Breadth Requirement List in any category except Math, Natural Sciences and Technology.
- Of the 21 credits, six credits must be at the Upper Level, defined as:
- any 300-level or higher course on the University Breadth Requirement list (excluding Math, Natural Sciences and Technology courses).
- any 300-level or higher course on the College of Engineering Breadth Requirement list (excluding Math, Natural Sciences and Technology courses).
- any foreign language instruction course at the 107 level or higher as designated on the College of Engineering Breadth Requirement list (some courses above the 107 level do NOT count toward this requirement because they are taught in English).
- A maximum of two courses (six credits) can be taken from the Career and Professional Preparation sub-section of the College of Engineering Breadth Requirement list to satisfy the College of Engineering additional breadth requirement.
- Of the 21 credits, three credits may be used to satisfy the University Multicultural Requirement (recommended for timely progress toward degree completion).
- With few exceptions, students may not use courses from their major to satisfy Breadth Requirement coursework.
IMPORTANT NOTE: Courses taken from the College of Engineering Breadth Requirement list can ONLY count toward the additional nine credits of breadth the College of Engineering requires for its majors. They CANNOT count for University Breadth.
Students pursuing any engineering major (except Computer Science or Information Systems) must have at least a 2.0 grade point average in all coursework that counts toward the Engineering Grade Point Average as seen on the degree audit. This coursework generally consists of engineering, mathematics, and science courses used to fulfill graduation requirements. The college adheres to the university grade forgiveness policy. Outside of the timeframe specified in that policy, if a course is repeated, only the last grade will be used to compute the Engineering Grade Point Average. Credit from courses taken pass/fail cannot be used to complete any engineering degree requirement, unless the course is only offered pass/fail in the engineering curriculum.
Computer Science Major Requirements:
The thesis option requires a CIS faculty member who agrees to supervise the thesis.
One of the following sequences (6 credits):
Lab Science Requirement:
Take one of the following 8-credit sequences:
Students pursuing the concentration in bioinformatics take BISC 207 , BISC 208 , and one of the chemistry sequences. The chemistry sequence fulfills the lab science requirement and the BISC sequence fulfills a requirement in the concentration.
ENGL 312 , ENGL 410 , and CISC 355 all count toward the College of Engineering Additional Breadth Requirement as Upper Level Breadth courses.
One of the following:
One of the following:
Take 12 credits from the following list.
Take 3-4 credits of CISC courses at the 300 level or higher.
No course may be applied to more than one of the departmental requirement categories.
After required courses are completed, sufficient elective credits must be taken to meet the minimum credit requirement for the degree.
Credits to Total a Minimum of 124
Last Revised for 2019-2020 Academic Year