Bachelor of Science in Mathematics and Computing
| Code | Title | Credit Hours |
|---|---|---|
| Wellness Requirement | ||
| APPH 1040 | Scientific Foundations of Health | 2 |
| or APPH 1050 | The Science of Physical Activity and Health | |
| or APPH 1060 | Flourishing: Strategies for Well-being and Resilience | |
| Core IMPACTS | ||
| Institutional Priority | ||
| CS 1301 | Introduction to Computing | 3 |
| Mathematics and Quantitative Skills | ||
| MATH 1552 | Integral Calculus | 4 |
| Political Science and U.S. History | ||
| HIST 2111 | The United States to 1877 | 3 |
| or HIST 2112 | The United States since 1877 | |
| or INTA 1200 | American Government in Comparative Perspective | |
| or POL 1101 | Government of the United States | |
| or PUBP 3000 | American Constitutional Issues | |
| Arts, Humanities, and Ethics | ||
| Any HUM | 6 | |
| Communicating in Writing | ||
| ENGL 1101 | English Composition I | 3 |
| ENGL 1102 | English Composition II | 3 |
| Technology, Mathematics, and Sciences | ||
| Lab Science | 8 | |
| MATH 1551 | Differential Calculus | 2 |
| or MATH 1550 | Introduction to Differential Calculus | |
| MATH 1554 | Linear Algebra | 4 |
| or MATH 1564 | Linear Algebra with Abstract Vector Spaces | |
| Social Sciences | ||
| Any SS | 9 | |
| Field of Study | ||
| CS 1331 | Introduction to Object Oriented Programming | 3 |
| CS 2110 | Computer Organization and Programming | 4 |
| MATH 2551 | Multivariable Calculus | 4 |
| or MATH 2561 | Honors Multivariable Calculus | |
| MATH 2552 | Differential Equations | 4 |
| or MATH 2562 | Honors Differential Equations | |
| MATH/CS 2740 | Foundations of Mathematics and Computing | 3 |
| Major Requirements | ||
| CS 3001 | Computing, Society, and Professionalism | 3 |
| CS 1332 | Data Structures and Algorithms for Applications | 3 |
| MATH 3406 | A Second Course in Linear Algebra | 3 |
| MATH 4317 | Analysis I | 3 |
| MATH/CX 3740 | Probability and Statistics for Computing and Machine Learning | 3 |
| Concentration Requirements | 27 | |
| Engineering or Science Electives 1 | 6 | |
| Free Electives | 9 | |
| Total Credit Hours | 122 | |
- 1
Any course at the 3000/4000 level with prefix AE, BIOS, BMED, CHBE, CHEM, CEE, COE, COS, EAS, ECON, ECE, ISYE, MSE, ME, NEUR, NRE, PHYS, PSYC except for ISYE 3770, CEE 3770, ECE 3077, any MATH, CS, CX courses.
Concentration Requirements
| Code | Title | Credit Hours |
|---|---|---|
| Theoretical Computer Science and Discrete Math Concentration | ||
| MATH 3012 | Applied Combinatorics | 3 |
| CS 2050 | Introduction to Discrete Mathematics for Computer Science | 3 |
| or CS 2051 | Honors - Induction to Discrete Mathematics for Computer Science | |
| CS 3510 | Design and Analysis of Algorithms | 3 |
| or CS 3511 | Design and Analysis of Algorithms, Honors | |
| CS 4510 | Automata and Complexity Theory | 3 |
| CS 4540 | Advanced Algorithms | 3 |
| Select two courses from List A: | 6 | |
| Algebraic Structures in Coding Theory | ||
| Introduction to Graph Theory | ||
| Combinatorial Analysis | ||
| Introduction to Abstract Algebra I | ||
| Introduction to Abstract Algebra II | ||
| Introduction to Number Theory | ||
| Mathematical Foundations of Data Science | ||
| Stochastic Processes I | ||
| Stochastic Processes II | ||
| Introduction to Information Theory | ||
| Analysis II | ||
| Linear Programming | ||
| Special Topics (Advanced Statistical Theory for Machine Learning) | ||
| Select two courses from List B: | 6 | |
| Introduction to Information Security | ||
| Introduction to Artificial Intelligence | ||
| Introduction to Cognitive Science | ||
| Machine Learning | ||
| Deep Learning | ||
| Introduction to High Performance Computing | ||
| Introduction to Computing for Data Analysis | ||
| Numerical Analysis I | ||
| Numerical Analysis II | ||
| Signals and Systems | ||
| Optimization for Information Systems | ||
| Advanced Optimization | ||
| Total Credit Hours | 27 | |
| Code | Title | Credit Hours |
|---|---|---|
| Modeling, Simulation, Data and Applied Math Concentration | ||
| MATH 4347 | Partial Differential Equations I | 3 |
| CX 4220 | Introduction to High Performance Computing | 3 |
| CX 4230 | Computer Simulation | 3 |
| MATH/CX 4640 | Numerical Analysis I | 3 |
| Select one course from ML List: | 3 | |
| Machine Learning | ||
| Introduction to Computing for Data Analysis | ||
| Mathematical Foundations of Data Science | ||
| Select two courses from List A: | 6 | |
| Applied Combinatorics | ||
| Introduction to Graph Theory | ||
| Stochastic Processes I | ||
| Stochastic Processes II | ||
| Introduction to Information Theory | ||
| Analysis II | ||
| Complex Analysis | ||
| Differential Geometry | ||
| Dynamics and Bifurcations I | ||
| Linear Programming | ||
| Mathematical Biology | ||
| Quantum Information and Quantum Computing | ||
| Special Topics (Advanced Statistical Theory for Machine Learning ) | ||
| Special Topics (Introduction to Stochastic Calculus ) | ||
| Select two courses from List B: | 6 | |
| Deep Learning | ||
| Computational Modeling Algorithms | ||
| Simulation and Military Gaming | ||
| Data and Visual Analytics | ||
| Numerical Analysis II | ||
| Computational Methods for Simulation and Machine Learning | ||
| Special Topics in Computational Science and Engineering (Inverse Problems) | ||
| Advanced Optimization | ||
| Earth System Modeling | ||
| Physics of the Earth | ||
| Total Credit Hours | 27 | |
| Code | Title | Credit Hours |
|---|---|---|
| Mathematical Intelligence and Data Science Concentration | ||
| CS 3600 | Introduction to Artificial Intelligence | 3 |
| MATH/CX 4740 | Computational Methods for Simulation and Machine Learning | 3 |
| Select one course from the following: | 3 | |
| Introduction to Perception and Robotics | ||
| Introduction to Cognitive Science | ||
| Sensation and Perception | ||
| Select one course from the ML list: | 3 | |
| Machine Learning | ||
| Introduction to Computing for Data Analysis | ||
| Mathematical Foundations of Data Science | ||
| Select one course from the following: | 3 | |
| Design and Analysis of Algorithms | ||
| Design and Analysis of Algorithms, Honors | ||
| Computational Modeling Algorithms | ||
| Select two courses from List A: | 6 | |
| Applied Combinatorics | ||
| Introduction to Graph Theory | ||
| Introduction to Abstract Algebra I | ||
| Stochastic Processes I | ||
| Stochastic Processes II | ||
| Introduction to Information Theory | ||
| Analysis II | ||
| Complex Analysis | ||
| Partial Differential Equations I | ||
| Differential Geometry | ||
| Dynamics and Bifurcations I | ||
| Linear Programming | ||
| Special Topics (Advanced Statistical Theory for Machine Learning) | ||
| Special Topics (Introduction to Stochastic Calculus) | ||
| Special Topics (Introduction to Geometric Methods in Machine Learning) | ||
| Special Topics (Introduction to Measure Transport and Generative Models) | ||
| Select two courses from List B: | 6 | |
| Introduction to Computer Vision | ||
| Advanced Algorithms | ||
| Knowledge-Based Artificial Intelligence | ||
| Deep Learning | ||
| Machine Learning for Trading | ||
| Robot Intelli Planning | ||
| Natural Language Understanding | ||
| Game AI | ||
| Numerical Analysis I | ||
| Numerical Analysis II | ||
| Special Topics in Computational Science and Engineering (Inverse Problems) | ||
| Fundamentals of Digital Signal Processing | ||
| Applications of Digital Signal Processing | ||
| Advanced Optimization | ||
| Total Credit Hours | 27 | |
BSMS Option
Undergraduate College of Engineering, College of Sciences and Computer Science majors and Master of Science with a major in Management
This option is open to all undergraduate College of Engineering and Computer Science students. Students must submit a BSMS application meet admissions criteria to be considered for the option.
Students may double count up to 6 credit hours of letter-grade 4000-level College of Engineering (if COE major), College of Sciences (if COS major), or Computer Science (if CS major) courses towards electives in the Master of Science with a major in Management (MS-MGT) program. Course selection for double-counted 4000-level courses must be approved by the MS-MGT program advisor. Students must still complete the 12 credit hour MS-MGT core.
Students are encouraged to reach out to the Scheller College of Business for more information.