CMSE 180 Introduction to Data Science
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 4 Lecture/Recitation/Discussion Hours: 4
Prerequisite:
(MTH 124 or concurrently) or (MTH 132 or concurrently) or (MTH 152H or concurrently) or (LB 118 or concurrently)
Not open to students with credit in:
STT 301
Description:
Pervasiveness and utility of data in modern society. Obtaining and managing data. Summarizing and visualizing data. Ethical issues in data science. Communication with data. Fundamentals of probability and statistics.
Interdepartmental With:
Statistics and Probability
Administered By:
Statistics and Probability
Effective Dates:
FS19 - Open
CMSE 201 Computational Modeling and Data Analysis I
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 4 Lecture/Recitation/Discussion Hours: 4
Prerequisite:
MTH 124 or MTH 132 or MTH 152H or LB 118
Description:
Computational modeling using a wide variety of applications examples. Algorithmic thinking, dataset manipulation, model building, data visualization, and numerical methods all implemented as programs.
Effective Dates:
FS20 - Open
CMSE 202 Computational Modeling and Data Analysis II
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 4 Lecture/Recitation/Discussion Hours: 4
Description:
Standard methods and tools for computational modeling and data analysis using simulation techniques, data mining, and machine learning.
Effective Dates:
FS20 - Open
CMSE 314 Matrix Algebra with Computational Applications
Semester:
Fall of every year, Spring of every year, Summer of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Prerequisite:
(MTH 133 or MTH 153H or LB 119) and (CMSE 201 or CSE 231)
Restrictions:
Not open to students in the Actuarial Science Major or in the Bachelor of Arts in Computational Mathematics or in the Bachelor of Science in Computational Mathematics or in the Bachelor of Science in Mathematics or in the Bachelor of Arts in Mathematics or in the Bachelor of Science in Mathematics, Advanced or in the Bachelor of Arts in Mathematics, Advanced or in the Mathematics Minor or in the Mathematics-Elementary Disciplinary Teaching Minor or in Mathematics-Secondary Disciplinary Teaching Minor.
Description:
Numerical methods in linear algebra with applications to systems of equations and eigenvalue problems, and geometry.
Interdepartmental With:
Mathematics
Administered By:
Mathematics
Effective Dates:
US19 - Open
CMSE 381 Fundamentals of Data Science Methods
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 4 Lecture/Recitation/Discussion Hours: 4
Prerequisite:
(STT 180 and MTH 314 and CMSE 201 and STT 380) or (STT 180 and MTH 314 and CMSE 201 and STT 441 and STT 442)
Description:
Data science methods, including unsupervised learning and supervised learning, feature extraction, dimension reduction, clustering, regression and classification.
Interdepartmental With:
Statistics and Probability
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
FS19 - Open
CMSE 382 Optimization Methods in Data Science
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 4 Lecture/Recitation/Discussion Hours: 4
Prerequisite:
CMSE 202 and CMSE 381
Description:
Concepts, mathematical foundations, methods, and algorithms of optimization in data modeling, all applied to modeling real-world data.
Effective Dates:
FS19 - Open
CMSE 401 Methods for Parallel Computing
Semester:
Spring of odd years
Credits:
Total Credits: 4 Lecture/Recitation/Discussion Hours: 4
Prerequisite:
(CMSE 202 and CSE 232) and (MTH 126 or MTH 133 or MTH 153H or LB 119)
Not open to students with credit in:
CSE 415
Description:
Core principles, techniques, and use of parallel computation using modern supercomputers. Parallel architectures and programming models. Message-passing and threaded programming. Principles of parallel algorithm design. Performance analysis and optimization.
Effective Dates:
FS21 - Open
CMSE 402 Data Visualization Principles and Techniques
Semester:
Spring of even years
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Prerequisite:
(CMSE 202) and (MTH 234 or MTH 254H or LB 220)
Description:
Core principles, methods, and techniques of effective data visualization. Visualization toolkits. Vector and scalar data. Multivariate visualization. Relationship between data analysis and visualization.
Effective Dates:
FS19 - Open
CMSE 404 Introduction to Machine Learning
Semester:
Fall of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Prerequisite:
(CSE 331) and (STT 351 or STT 380 or STT 430 or STT 441) and MTH 314
Recommended Background:
Basic linear algebra
Restrictions:
Open to juniors or seniors in the College of Engineering or in the Computer Science Minor or in the Lyman Briggs Computer Science Coordinate Major or in the Lyman Briggs Computer Science Major or in the Data Science Major.
Description:
Core principles and techniques for machine learning including algorithms, model design, and programming.
Interdepartmental With:
Computer Science and Engineering, Statistics and Probability
Administered By:
Computer Science and Engineering
Effective Dates:
FS21 - Open
CMSE 410 Bioinformatics and Computational Biology
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 2 Lab Hours: 2
Prerequisite:
{(CMSE 201 and LB 144 and LB 145) or (CMSE 201 and BS 161 and BS 162) or (CMSE 201 and BS 181H and BS 182H)} and (STT 200 or STT 201 or STT 231 or STT 421 or STT 351 or ECE 280)
Description:
Computational approaches in modern biology with a focus on applications in genomics, systems biology, evolution, and structural biology.
Interdepartmental With:
Biochemistry and Molecular Biology, Microbiology and Molecular Genetics, Plant Biology
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
FS20 - US25
CMSE 411 Computational Medicine
Semester:
Fall of even years
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Prerequisite:
(CMSE 201 and LB 144 and LB 145) or (CMSE 201 and BS 161 and BS 162) or (CMSE 201 and BS 181H and BS 182H)
Description:
Computational approaches in biology with a focus on medicine.
Interdepartmental With:
Biochemistry and Molecular Biology, Microbiology and Molecular Genetics
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
FS21 - US25
CMSE 491 Selected Topics in Computational Mathematics, Science, and Engineering
Semester:
Fall of every year, Spring of every year
Credits:
Variable from 1 to 4
Reenrollment Information:
A student may earn a maximum of 12 credits in all enrollments for this course.
Description:
Topics selected to supplement and enrich existing courses and lead to the development of new courses.
Effective Dates:
FS18 - Open
CMSE 492 Selected Topics in Data Science
Semester:
Fall of every year, Spring of every year
Credits:
Variable from 1 to 4
Reenrollment Information:
A student may earn a maximum of 12 credits in all enrollments for this course.
Restrictions:
Approval of department.
Description:
Topics selected to supplement and enrich existing courses in Data Science.
Interdepartmental With:
Computer Science and Engineering, Statistics and Probability
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
FS19 - Open
CMSE 494 Independent Study in Data Science
Semester:
Fall of every year, Spring of every year, Summer of every year
Credits:
Variable from 1 to 3
Reenrollment Information:
A student may earn a maximum of 3 credits in all enrollments for this course.
Restrictions:
Open to students in the Computational Data Science Major or in the Computer Engineering Major or in the Computer Science Major or in the Data Science Major. Approval of department; application required.
Description:
Supervised individual study in an area of Data Science
Interdepartmental With:
Computer Science and Engineering
Administered By:
Computer Science and Engineering
Effective Dates:
FS24 - Open
CMSE 495 Experiential Learning in Data Science (W)
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 4 Lecture/Recitation/Discussion Hours: 2 Lab Hours: 4
Prerequisite:
(CSE 232 and CMSE 382) and completion of Tier I writing requirement
Restrictions:
Open to seniors.
Description:
Team-based data science projects working with real-world data in collaboration with client/company sponsors. Practice in software development, data collection, curation, modeling, scientific visualization and presentation of results. Students may be required to sign a non-disclosure agreement (“NDA”) or an assignment of intellectual property rights (“IP Assignment”) to work with some project sponsors.
Interdepartmental With:
Computer Science and Engineering, Statistics and Probability
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
SS23 - Open
CMSE 499 Independent Study in Computational Mathematics, Science, and Engineering
Semester:
Fall of every year, Spring of every year
Credits:
Variable from 1 to 4
Reenrollment Information:
A student may earn a maximum of 6 credits in all enrollments for this course.
Restrictions:
Approval of department.
Description:
Supervised individual research or study in an area of computational or data science.
Effective Dates:
FS16 - Open
CMSE 801 Introduction to Computational Modeling and Data Analysis
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
One semester of introductory calculus
Description:
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.
Effective Dates:
US20 - Open
CMSE 802 Methods in Computational Modeling
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
(CMSE 801) or equivalent experience
Description:
Standard computational modeling methods and tools. Programming and code-management techniques.
Effective Dates:
FS19 - Open
CMSE 820 Mathematical Foundations of Data Science
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
CMSE 802 or equivalent experience in programming and
numerical methods. Differential equations at the level of (MTH 235 or MTH 255H or (MTH 340 and MTH 442) or (MTH 347H and MTH 442)). Linear algebra at the level of (MTH 309 or MTH 317H). Probability and statistics at the level of STT 231.
Description:
Fundamental mathematical principles of data science that underlie the algorithms, processes, and methods of data-centric thinking, and tools based on these principles.
Effective Dates:
US17 - Open
CMSE 821 Numerical Methods for Differential Equations
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
CMSE 802 or equivalent experience in programming and numerical methods. Differential equations at the level of (MTH 235 or MTH 255H or (MTH 340 and MTH 442) or (MTH 347H and MTH 442)). Linear algebra at the level of (MTH 309 or MTH 317H)
Description:
Numerical solution of ordinary and partial differential equations, including hyperbolic, parabolic, and elliptic equations. Explicit and implicit solutions. Numerical stability.
Effective Dates:
US17 - Open
CMSE 822 Parallel Computing
Semester:
Fall of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
Calculus at the level of MTH 133. 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.
Description:
Core principles, techniques, and use of parallel computation using modern supercomputers. Parallel architectures. Parallel programming models. Principles of parallel algorithm design. Performance analysis and optimization.
Interdepartmental With:
Computer Science and Engineering
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
US17 - Open
CMSE 823 Numerical Linear Algebra
Semester:
Fall of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
(CMSE 802) or equivalent experience in programming and numerical methods. Linear algebra at the level of MTH 309 or MTH 317H.
Description:
Methods in modern numerical linear algebra for solving linear systems, least squares problems, and eigenvalue problems. Efficiency and stability of algorithms in numerical linear algebra.
Effective Dates:
US17 - Open
CMSE 830 Foundations of Data Science
Semester:
Fall of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
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 the Computational Mathematics, Science and Engineering.
Description:
Core mathematical principles that underlie the algorithms and methods used in data science. Applications to problems in data analysis.
Effective Dates:
SS20 - Open
CMSE 831 Computational Optimization
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
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)
Description:
Applications and algorithms for finite-dimensional linear and non-linear optimization problems.
Effective Dates:
SS20 - Open
CMSE 841 Foundation in Computational and Plant Sciences
Semester:
Fall of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Description:
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
Effective Dates:
FS20 - Open
CMSE 843 Forum in Computational and Plant Sciences
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits: 1 Lecture/Recitation/Discussion Hours: 1
Reenrollment Information:
A student may earn a maximum of 4 credits in all enrollments for this course.
Description:
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
Effective Dates:
FS20 - Open
CMSE 844 Frontiers in Computational and Plant Sciences
Semester:
Spring of every year
Credits:
Total Credits: 3 Lecture/Recitation/Discussion Hours: 3
Recommended Background:
Basic programming, mathematical modeling, and statistics
Description:
Interdisciplinary research interfacing computational and plant sciences. Molecular system biology, phenomics, and mechanisms connecting genotype and phenotype
Interdepartmental With:
Crop and Soil Sciences, Biochemistry and Molecular Biology, Horticulture, Plant Biology
Administered By:
Crop and Soil Sciences
Effective Dates:
SS20 - Open
CMSE 890 Selected Topics in Computational Mathematics, Science, and Engineering
Semester:
Fall of every year, Spring of every year
Credits:
Variable from 1 to 4
Reenrollment Information:
A student may earn a maximum of 12 credits in all enrollments for this course.
Description:
Topics selected to supplement and enrich existing courses.
Effective Dates:
SS18 - Open
CMSE 891 Independent Study in Computational Mathematics, Science, and Engineering
Semester:
Fall of every year, Spring of every year
Credits:
Variable from 1 to 4
Reenrollment Information:
A student may earn a maximum of 6 credits in all enrollments for this course.
Restrictions:
Approval of department.
Description:
Topics selected to supplement and enrich existing courses.
Effective Dates:
US17 - Open
CMSE 899 Master's Thesis Research
Semester:
Fall of every year, Spring of every year, Summer of every year
Credits:
Variable from 1 to 6
Reenrollment Information:
A student may earn a maximum of 8 credits in all enrollments for this course.
Restrictions:
Open to master's students in the Department of Computational Mathematics, Science, and Engineering.
Description:
Master's thesis research
Effective Dates:
US17 - Open
CMSE 999 Doctoral Dissertation Research
Semester:
Fall of every year, Spring of every year, Summer of every year
Credits:
Variable from 1 to 24
Reenrollment Information:
A student may earn a maximum of 36 credits in all enrollments for this course.
Restrictions:
Open to doctoral students in the Department of Computational Mathematics, Science, and Engineering.
Description:
Doctoral dissertation research.
Effective Dates:
US17 - Open