CMSE Graduate Courses

The Department of Computational Mathematics, Science and Engineering currently offers several courses pertaining to computational and data science, as described below or check the course catalog. Special topics courses are offered as CMSE 890 and topics change every semester depending on student interest and faculty availability.

Please contact the CMSE Graduate Director for additional information or to discuss your eligibility for registering for courses.

CMSE Graduate Courses

CMSE 801 - Introduction to Computational Modeling.  Introduction to computational modeling using a wide variety of application examples. Algorithmic thinking and model building, data visualization, numerical methods, all implemented as programs. Command line interfaces. Scientific software development techniques including modular programming, testing, and version control.

Recommended background: one semester of introductory calculus.  

(3 credits)  Offered every fall and spring semester.

 

CMSE 802 - Methods in Computational Modeling.  Standard computational modeling methods and tools. Programming and code-management techniques.

Recommended background:  CMSE 801 or equivalent experience.  

(3 credits)  Offered every fall and spring semester.

 

CMSE 820 - Mathematical Foundations of Data Science. Introduces students to the fundamental mathematical principles of data science that underlie the algorithms, processes, methods, and data-centric thinking. Introduces students to algorithms and tools based on these principles.  

Recommended background:  CMSE 802 or equivalent experience.  Differential equations at the level of MTH 235/255H/340+442/347H+442.  Linear algebra at the level of MTH 390/317H.  Probability and statistics at the level of STT 231.  

(3 credits)  Offered every fall and spring semester.

 

CMSE 821 - Numerical Methods for Differential Equations. Numerical solution of ordinary and partial differential equations, including hyperbolic, parabolic, and elliptic equations. Explicit and implicit solutions. Numerical stability.   

Recommended background:  CMSE 802 or equivalent experience.  Differential equations at the level of MTH 235/255H/340+442/347H+442.  Linear algebra at the level of MTH 390/317H.  

(3 credits)  Offered every spring semester.

 

CMSE/CSE 822 - Parallel Computing.  Core principles and techniques of parallel computation using modern supercomputers. Parallel architectures. Parallel programming models. Principles of parallel algorithm design. Performance analysis and optimization. Use of parallel computers.  

Recommended background: One semester of introductory calculus. Ability to program proficiently in C/C++, basic understanding of data structures and algorithms (both at the level of CSE 232). Basic linear algebra and differential equations.  

(3 credits)  Offered every fall semester.

 

CMSE 823 - Numerical Linear Algebra, I.  Convergence and error analysis of numerical methods in applied mathematics.  

Recommended background: CMSE 802 or equivalent experience; Linear algebra at the level of MTH 414.

(3 credits) Offered every fall semester.

 

CMSE 830 - Foundations of Data Science.  Core mathematical principles that underlie the algorithms and methods used in data science. Applications to problems in data analysis.

Recommended background: (CMSE 201 or CSE 231 or CMSE 801) and (MTH 235 or MTH 340 or MTH 347H) and ((MTH 309 or MTH 314 or MTH 317H) and STT 810)

Restrictions: Not open to doctoral students in Computational Mathematics, Science and Engineering.

(3 credits)   Offered every fall semester.

 

CMSE 831 - Computational Optimization.  Applications and algorithms for finite-dimensional linear and non-linear optimization problems.

Recommended background: (CMSE 201 or CMSE 801 or CSE 231) and (MTH 235 or MTH 340 or MTH 347H) and ((MTH 309 or MTH 314 or MTH 317H) and STT 810) 

Restrictions: Not open to doctoral students in Computational Mathematics, Science and Engineering.

(3 credits)   Offered every spring semester.

CMSE 841 - Foundation in Computational and Plant Sciences.  Computational modeling applied to plant biology. Data analysis, algorithmic thinking, model building, bioinformatics, and molecular biology using coding and computational resources. 

Interdepartmental with: Horticulture, Biochemistry and Molecular Biology, Plant Biology, Crop and Soil Sciences

Administered by: Horticulture

(3 credits)   Offered every fall semester.

 

CMSE 843 - Forum in Computational and Plant Sciences.  Professional development focused on diverse modes of communication in support of interdisciplinary science with an emphasis on plant and computational sciences. 

Interdepartmental with: Plant Biology, Biochemistry and Molecular Biology, Horticulture, Crop and Soil Sciences

Administered by: Plant Biology

(3 credits)   Offered every fall and spring semester.

 

CMSE 843 - Frontiers in Computational and Plant Sciences.  Interdisciplinary research interfacing computational and plant sciences. Molecular system biology, phenomics, and mechanisms connecting genotype and phenotype 

Recommended background: Basic programming, mathematical modeling, and statistics 

Interdepartmental with: Crop and Soil Sciences, Biochemistry and Molecular Biology, Horticulture, Plant Biology

Administered by: Crop and Soil Sciences

(3 credits)   Offered every spring semester.

 

CMSE 890 - Selected Topics in Computational Mathematics, Science, and Engineering.  Topics selected to supplement and enrich existing courses and lead to the development of new courses.  

Recommended background varies with topic and instructor.  

(1-4 credits)  Offerings vary; consult the /academics/special-courses/">Special Courses  or Registrar's website. 

Note: A student may earn a maximum of 12 credits in all enrollments for this course.

 

CMSE 891 - Independent Study in Computational Mathematics, Science, and Engineering. 

(1-4 credits)  Enrollment by approval only.

 

CMSE 899 - Master's Thesis Research. Master's thesis research.  

(1-6 credits)  Enrollment by approval only. 

Note: A student may earn a maximum of 8 credits in all enrollments for this course.

 

CMSE 999 - Doctoral Dissertation Research.  Doctoral dissertation research.  

(1-24 credits)  Enrollment by approval only. 

Note: A student may earn a maximum of 36 credits in all enrollments for this course.