Second Major in Computer Science + Mathematics (CSE)
This second major, developed through a collaboration between the McKelvey School of Engineering and the College of Arts & Sciences, efficiently captures the intersection of the complementary studies of computer science and math.
McKelvey Engineering students who declare this major must fulfill the core course requirements listed below and all other requirements for the Applied Science degree in the McKelvey School of Engineering. They must also complete ENGR 3100 Technical Writing and 8 units of courses designated as NSM (Natural Sciences & Math) from Anthropology (ANTHRO), Biology and Biomedical Sciences (BIOL), Chemistry (CHEM), Earth, Environmental, and Planetary Sciences (EEPS), Physics (PHYSICS), or Environmental Studies (ENST).
Arts & Sciences students who declare this major must fulfill the distribution requirements and all other requirements for the Bachelor of Arts (BA) degree in addition to the specific requirements listed below.
Core Course Requirements*
| Code | Title | Units |
|---|---|---|
| CSE 1301 | Introduction to Computer Science | 3 |
| CSE 2400 | Logic and Discrete Mathematics ** | 3 |
| CSE 2407 | Data Structures and Algorithms | 3 |
| CSE 3407 | Analysis of Algorithms | 3 |
| MATH 1510 | Calculus I ** | 3 |
| MATH 1520 | Calculus II ** | 3 |
| MATH 2130 | Calculus III ** | 3 |
| MATH 3010 Foundations for Higher Mathematics ** or MATH 3015 Foundations for Higher Mathematics With Writing ** | 3 | |
| MATH 3300 | Matrix Algebra ** | 3 |
| SDS 3020 Elementary to Intermediate Statistics and Data Analysis or SDS 3030 Statistics for Data Science I or ESE 3260 Probability and Statistics for Engineering | 3 | |
| Total Units | 30 | |
- *
Each of these core courses must be passed with a C- or better.
- **
AP credit may be applied in place of MATH 1510 and/or MATH 1520. Students who complete the MATH 2801 Honors Mathematics I and MATH 2802 Honors Mathematics II sequence will be considered to have completed MATH 1510, MATH 1520, MATH 2130, and CSE 2400; these students are also recommended to bypass MATH 3010/MATH 3015 and MATH 3300, for which they may substitute any other upper-level Mathematics courses.
Electives
Seven upper-level courses from Math or Computer Science & Engineering can be chosen from the approved list, with the following caveats:
- At least three courses must be taken from CSE and at least three courses must be taken from Math.
- At most one preapproved course from outside both departments can be selected.
- CSE 4000 or CSE 4001 Independent Study may be taken for a maximum of 3 units and must be approved by a CS+Math review committee.
- For each of the following pairs of electives, students may count one as an elective toward the major but not both:
- CSE 2107 Introduction to Data Science or BME 4400 Biomedical Data Science
- CSE 4107 Introduction to Machine Learning or ESE 4170 Introduction to Machine Learning and Pattern Classification
- CSE 4109 Introduction to AI for Health or CSE 5310 AI for Health
- MATH 4560 Topics in Financial Mathematics or ESE 4270 Financial Mathematics
List of Approved Electives
Computer Science & Engineering
| Code | Title | Units |
|---|---|---|
| CSE 2107 | Introduction to Data Science | 3 |
| CSE 3401 | Parallel and Sequential Algorithms | 3 |
| CSE 4061 | Text Mining | 3 |
| CSE 4101 | AI and Society | 3 |
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4106 | Data Science for Complex Networks | 3 |
| CSE 4107 | Introduction to Machine Learning | 3 |
| CSE 4109 | Introduction to AI for Health | 3 |
| CSE 4207 | Cloud Computing with Big Data Applications | 3 |
| CSE 4402 | Introduction to Cryptography | 3 |
| CSE 4470 | Introduction to Formal Languages and Automata | 3 |
| CSE 4507 | Introduction to Visualization | 3 |
| CSE 4608 | Introduction to Quantum Computing | 3 |
| CSE 5100 | Deep Reinforcement Learning | 3 |
| CSE 5103 | Theory of Artificial Intelligence and Machine Learning | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5105 | Bayesian Methods in Machine Learning | 3 |
| CSE 5106 | Multi-Agent Systems | 3 |
| CSE 5107 | Machine Learning | 3 |
| CSE 5108 | Human-in-the-Loop Computation | 3 |
| CSE 5270 | Natural Language Processing | 3 |
| CSE 5313 | Coding and Information Theory for Data Science | 3 |
| CSE 5310 | AI for Health | 3 |
| CSE 5401 | Advanced Algorithms | 3 |
| CSE 5403 | Algorithms for Nonlinear Optimization | 3 |
| CSE 5404 | Special Topics in Computer Science Theory | 3 |
| CSE 5406 | Computational Geometry | 3 |
| CSE 5504 | Geometric Computing for Biomedicine | 3 |
| CSE 5505 | Adversarial AI | 3 |
| CSE 5509 | Computer Vision | 3 |
| CSE 5519 | Advances in Computer Vision | 3 |
| CSE 5610 | Large Language Models | 3 |
| CSE 5801 | Approximation Algorithms | 3 |
| CSE 5804 | Algorithms for Biosequence Comparison | 3 |
| CSE 5807 | Algorithms for Computational Biology | 3 |
| ESE 5130 | Large-Scale Optimization for Data Science | 3 |
Mathematics
| Code | Title | Units |
|---|---|---|
| MATH 3410 | Introduction to Combinatorics | 3 |
| MATH 3420 | Graph Theory | 3 |
| MATH 3590 | Topics in Applied Mathematics | 3 |
| MATH 4101 | Introduction to Analysis | 3 |
| MATH 4102 | Introduction to Lebesgue Integration | 3 |
| MATH 4150 | Introduction to Fourier Series and Integrals | 3 |
| MATH 4201 | Topology I | 3 |
| MATH 4220 | An Introduction to Differential Geometry | 3 |
| MATH 4301 | Linear Algebra | 3 |
| MATH 4302 | Modern Algebra | 3 |
| MATH 4350 | Number Theory and Cryptography | 3 |
| MATH 4493 | Topics in Graph Theory | 3 |
| MATH 4501 | Numerical Applied Mathematics | 3 |
| MATH 4502 | Topics in Applied Mathematics | 3 |
| MATH 4560 | Topics in Financial Mathematics | 3 |
| MATH 4570 | The Mathematics of Quantum Theory | 3 |
| SDS 4010 | Probability | 3 |
| SDS 4720 | Stochastic Processes | 3 |
Statistics and Data Science
| Code | Title | Units |
|---|---|---|
| SDS 4010 | Probability * | 3 |
| SDS 4020 | Mathematical Statistics | 3 |
| SDS 4110 | Experimental Design | 3 |
| SDS 4120 | Survival Analysis | 3 |
| SDS 4130 | Linear Statistical Models | 3 |
| SDS 4140 | Advanced Linear Statistical Models | 3 |
| SDS 4155 | Time Series Analysis | 3 |
| SDS 4210 | Statistical Computation | 3 |
| SDS 4310 | Bayesian Statistics | 3 |
| SDS 4430 | Statistical Learning | 3 |
| SDS 4440 | Mathematical Foundations of Data Science | 3 |
| SDS 4720 | Stochastic Processes * | 3 |
- *
This course may be counted as a Mathematics elective.
Electrical & Systems Engineering
| Code | Title | Units |
|---|---|---|
| ESE 4031 | Optimization for Engineered Planning, Decisions and Operations | 3 |
| ESE 4150 | Optimization | 3 |
| ESE 4170 | Introduction to Machine Learning and Pattern Classification | 3 |
| ESE 4270 | Financial Mathematics | 3 |
| ESE 4290 | Basic Principles of Quantum Optics and Quantum Information | 3 |
| ESE 5130 | Large-Scale Optimization for Data Science * | 3 |
| ESE 5200 | Probability and Stochastic Processes | 3 |
- *
This course may be counted as a Computer Science & Engineering elective.
Economics
| Code | Title | Units |
|---|---|---|
| ECON 4151 | Applied Econometrics | 3 |
| ECON 4710 | Game Theory | 3 |
Linguistics
| Code | Title | Units |
|---|---|---|
| LING 3250 | Introduction to Computational Linguistics | 3 |
| LING 4250 | Computation and Learnability in Linguistic Theory | 3 |
Biomedical Engineering
| Code | Title | Units |
|---|---|---|
| BME 4400 | Biomedical Data Science | 3 |
| BME 4700 | Mathematics of Imaging Science | 3 |
| BME 5720 | Biological Neural Computation | 3 |
Physics
| Code | Title | Units |
|---|---|---|
| PHYSICS 4027 | Introduction to Computational Physics | 3 |
Additional Departmental Requirements
| ENGR 3100 Technical Writing | 3 |
| One themed writing course from the College Writing Program | 3 |
| Humanities and social sciences electives | 18 |
| Natural sciences electives | 8 |
The College Writing Program, humanities, and social sciences requirements are those required of all students in the McKelvey School of Engineering. For information about how to fulfill the school's English proficiency requirement, please visit the Degree Requirements page of this Bulletin.
The natural sciences requirement is for 8 units designated NSM (Natural Sciences and Mathematics) from any of the following departments: Anthropology; Biology; Chemistry; Earth, Environmental, and Planetary Sciences; Environmental Studies; or Physics. The College Writing Program and natural sciences courses must be completed with a grade of C- or better.
All courses taken to meet any of the above requirements (with the exception of the humanities and social sciences electives) cannot be taken on a pass/no pass basis.