Special courses

Special courses

The following special courses pertaining to computational and data science that may be of interest to students both inside and outside of CMSE.  We anticipate that a significant number of additional courses will be added each year - check the course catalog for more information!

Please contact the CMSE Graduate Director for additional information and updates.

Note: many of these classes are not directly affiliated with the CMSE program.

FOR 875 (Sect. 730) - R Programming for Data Science

This summer, the Forestry Department is offering an online course for R programming that might be of interest to CMSE undergraduate and graduate students. Please contact Andrew Finley with questions at finleya@msu.edu.

Spring 2018
ECE 802 Sect 612 Selected Topics: Inverse Problem and Imaging

The aim of this course is to introduce the theory of inverse problems and methods for solving them in practice. The student, upon completion of the course, should be able to comprehend the principles underlying ill-posed problems and the imaging of material objects. Particular attention will be paid in the course in designing numerical inversion algorithms.

ECE 929D: Fast Computational Methods in Electromagnetics and Acoustics

Course instructor: B. Shanker, bshanker@egr.msu.edu

Date/time: Tuesday/Thursday, 1:00-2:20 p.m. in 004 Urban Plan & Land Arch

Prerequisites: Numerical Linear Algebra

CMSE 890 (Sect. 001) - Image Processing Techniques - SS18

The CMSE Department will be offering a graduate course on Image Processing Techniques taught by faculty member Dirk Colbry. In this course, we intend to develop and explore tools that assist researchers in analyzing their scientific image datasets. To do this we are focusing on the computational representation of images and the types and classes of algorithms that have been developed for science analysis.

MTH 994 (Sect. 003) - Computational Harmonic Analysis and Data Science - SS18

This course will cover aspects of modern computational harmonic analysis at the interface of signal processing and data science. A central theme of the course is to find “good” representations of functional data (e.g., time series, images, etc), where the quality of the representation is measured through notions of sparsity, characterization of certain functional classes, and eventually empirical data driven measures.

NEU 425 - Theory and computational models in neuroscience - SS18

The aims of this course are to introduce modeling techniques and issues, review successful models, and develop critical thinking about computational models and theories in neuroscience. We will study and experiment with simple models of single cells and small networks. We will then study some models of simple animal behaviors and conclude with more complex models for mammalian cognition. Students will learn and work in MATLAB and will make their own model of a specific dynamic process or behavior for a course project.

NEU 445 - Analysis of Neuroscience Data - SS18

Today’s technologies enable neuroscientists to gather data in quantities previously unimagined, and the BRAIN initiative will dramatically expand these capabilities. Many experimental researchers experience a bottleneck when it comes time to analyze their hard-won data. This course is intended to help neuroscience practitioners to meet the challenges posed by the analysis of large functional datasets, and to introduce quantitative students to the wealth of problems to be addressed in big data from neuroscience.

Fall 2017
BE 835 - Modeling Methods in Bio Engineering - FS17

Numerical methods, parameter estimation, and statistical analysis for mathematical models used in engineering. Theory will be illustrated with examples from various engineering disciplines. MATLAB will be taught and used in the class, and is required to be used for homeworks and exams.

CMSE491 (Section 002) - Numerical Linear Algebra

This is an introductory course on Linear Algebra with a focus on scientific/engineering applications and solving large problems using computers. This course will be instructed by Faculty Member, Dirk Colbry.

CMSE 890 (Sect. 001) - Optimization - FS17

The CMSE Department will be offering a graduate course on Optimization Techniques taught by faculty member Ming Yan. This course aims at providing students modeling skills, optimization algorithms, and theories in optimization and its applications in big data analysis, with a special emphasis on deep understanding on various optimization algorithms.

CMSE 890 (Sect. 301) - Programming Foundations for Bioinformatics - FS17

The CMSE Department will be offering a graduate course on Programming Foundations for Bioinformatics taught by faculty member Alexis Black Pyrkosz. This is an introductory course for scientists to learn programming in Unix and R with examples in bioinformatics.

CMSE 890 (Sect. 302) - Statistical Analysis and Visualization of Biological Data - FS17

The CMSE Department will be offering a graduate course on Statistical Analysis and Visualization of Biological Data taught by faculty member Alexis Black Pyrkosz. This is an introductory course for scientists to learn statistics that are relevant to bioinformatics.

CMSE 890 (Sect. 303) - Transcriptomic Data Analysis: from Reads to Functions - FS17

The CMSE Department will be offering a graduate course on Transcriptomic Data Analysis: from Reads to Functions taught by faculty member Alexis Black Pyrkosz.  This is an introductory course for scientists to learn how to turn raw RNA-Seq data into gene expression levels, differentially expressed genes, enriched pathways, and gene/sample clusters.

Spring 2017
CEM 993 - Advanced Topics of Quantum Theory, Chemistry - SS17

For student who are interested in learning how to better appreciate modern methods of many-body quantum mechanics, which are widely used in high accuracy first principles computations for atoms, molecules, condensed matter systems, and nuclei.

CHE 891 (Sect. 002) - Viscoelastic Fluids - SS17

The course is intended for advanced graduate students with an interest in developing a fundamental understanding of viscoelasticity. The class discussion will be supported by selected readings from the literature. Each student will have an opportunity to present oral presentations related to their emerging interest in memory fluids.

NEU 445 - Analysis of Neuroscience Data - SS17

Today’s technologies enable neuroscientists to gather data in quantities previously unimagined, and the BRAIN initiative will dramatically expand these capabilities. Many experimental researchers experience a bottleneck when it comes time to analyze their hard-won data. This course is intended to help neuroscience practitioners to meet the challenges posed by the analysis of large functional datasets, and to introduce quantitative students to the wealth of problems to be addressed in big data from neuroscience.

PHY 905 (Sect. 003) - Numerical Techniques in Astrophysics - SS17

Numerical solutions and statistical methods as applied to key problems in astronomy and astrophysics. N-body gravitational calculations, hydrodynamics in astronomy, radiative transfer.  Monte Carlo, Bayesian statistics, techniques for large datasets.