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.
- BE 835 - Modeling Methods in Bio Engineering - FS16
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.
- CMSE 890 (Sect. 001) - Programming for Multi-Core Architectures - FS16
The CMSE department and the Blue Waters project at the University of Illinois is will be offering of a graduate course Algorithmic Techniques for Scalable Many-core Computing as a collaborative, online course for multiple participating institutions. The course includes online video lectures, quizzes, and homework assignments with access to free accounts on the Blue Waters system.
- CSE 836 - Computational Comparative Genomics with Applications in Biology and Biomedicine - FS16
Can an algorithm save lives, fight disease, or shed light on human origins? This course covers computational aspects of comparative genomic analysis, an important tool used to investigate these and other cutting-edge questions in biology and human health. A primary goal of the course is to introduce quantitative foundations that underly this area and, more generally, bioinformatics and computational biology.
- FOR/STT 875 - R Programming for Data Sciences - FS16
Programming in R and use of associated Open Source tools. Addressing practical issues in documenting workflow, data management, and scientific computing.
- NEU 425 - Theory and computational models in neuroscience - FS16
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.
- PLB 400/810 - Theories and practices in Bioinformatics - FS16
Introduction of the theories and algorithms behind widely used bioinformatics tools. Basic tool development by writing scripts in the Python programming language for data analysis.
- NEU 445 - Analysis of Neuroscience Data - FS15
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.