Upcoming seminars and workshops

CMSE 890-005: Applied Machine Learning in the Physical & Life Sciences

This special topics course addresses shallow and deep learning. Expect weekly in-class coding projects and a capstone project with poster presentation.

CMSE 802 - Methods Computational Modeling

Computational science uses computers to solve problems, simulate phenomena, and create knowledge. Over the course of this semester, we will explore various aspects of computational science. Participants will learn standard modeling methods and tools, as well as programming (in Python), code-management, and basic data science techniques. Participants will apply these techniques and skills to their own research.

Introduction to Linux

Learn to navigate the UNIX file system and write a basic shell script as a prerequisite for submitting computational jobs on the HPCC.

Matplotlib for Data Visualization

Matplotlib is a Python library for creating 2D-graphics. Learn how to visualize data in Python and produce publication-quality figures.

Monthly Workshop: Introduction to HPCC

This is a hands-on introductory workshop on using MSU’s High Performance Computing Center (HPCC).

PC2HPC: Parallel Computing

This seminar will introduce the basic concepts of parallel computing and demonstrate these concepts through shell script examples.

Gaussian on HPCC

This is a hands-on workshop for applying quantum chemistry packages such as Gaussian to accurately predict molecular energies, structures, vibrational and electronic spectra. Participants will learn how to run these software efficiently on MSU's HPCC system.

Introduction to Linux

Learn to navigate the UNIX file system and write a basic shell script as a prerequisite for submitting computational jobs on the HPCC.

Monthly Workshop: Introduction to HPCC

This is a hands-on introductory workshop on using MSU’s High Performance Computing Center (HPCC).

R on HPCC

Learn about using R on the MSU’s High Performance Computing system via the command line and batch jobs.

CMSE 890-310: GAPS, MISSTEPS, AND ERRORS IN STATISTICAL DATA ANALYSIS

A short 1-credit (4-week) course designed to: (i) discuss common misunderstandings & typical errors in the practice of statistical data analysis; and (ii) provide a mental toolkit for critically thinking about statistical methods and results.

Introduction to Linux

Learn to navigate the UNIX file system and write a basic shell script as a prerequisite for submitting computational jobs on the HPCC.