ME Elective Course Descriptions

Instructor: Markus Nemitz

Schedule: Monday & Wednesday 1:30 - 2:45 PM

Description: This course delivers in-depth training in 3D printing for soft robotics, focusing on designing, fabricating, and modeling printable fluidic sensors, actuators, controllers, and their integration into robotic systems.

Instructor: Trevion Henderson

Schedule: Tuesday & Thursday 12 - 1:15 PM

Description:  This course will provide students with opportunities to develop and advance their quantitative research skills for applied empirical research in the social sciences. The course will focus specifically on applications to STEM Education research. Learning activities will include hands-on practice conducing select quantitative analyses using Stata, reading and critically reviewing existing literature, and writing and presenting quantitative research findings. Students will be able to develop and frame research questions, plan and carry out quantitative data collection strategies, compute and present summary statistics, conduct bivariate statistical tests, estimate multiple linear regression models, use Stata to conduct quantitative analyses. Topics will include level of measurement, univariate statistics (e.g., measures of central tendency and dispersion), bivariate statistical tests (e.g., correlations, chi-square, t-tests, ANOVA), multiple regression, research ethics, reliability and validity, survey of advanced statistics methods. The course’s major learning activity will be an end-of-term manuscript. While the instructor will provide datasets for all assignments, students interested in using their own data to plan, conduct, and report analyses for conference proposals, journal manuscripts, or other venues are encouraged to do so.

Instructor: Chris Rogers

Schedule: Tuesday & Thursday 10:30 - 11:45 AM

Description: In this class, we will build a bunch of robots and see how far we can go with different machine learning algorithms to control them since robotics companies are moving from classical controls to neural nets and reinforcement learning. We will start with supervised and un-supervised learning models and move on to reinforcement learning, neural nets and end up with training a Large Behavior Model to control our bots. We will start with using LEGO building system but then end with designing and fabricating autonomous robots around the Arduino UnoQ. Note that a lot of this class will be around the fabrication of the robots - those interested in delving deeply into the code should take the dedicated CS courses.