A talk I gave at the Wolfram Technology Conference, 2019.

In our “Computational Methods for Psychology and Neuroscience” course, we teach undergraduate students the fundamentals of computational thinking (as opposed to traditional “programming”) using a project-based approach. Over the years project topics have ranged from linguistics, video image analysis, Dynamic[] driven data collection, analysis and presentation, machine learning, and beyond. Most recently, we chose colorimetry and psychophysics as our project theme. Using the Connected Devices framework and an Arduino for data collection, we build a machine learning model from publicly available hyperspectral data that could reliably discriminate fruit types from simple, low-dimensional spectral scans.

The resulting project was well received by students and covered a broad range of topics that are useful in neuroscience including: procedural programming of the Arduino, basic electronics, sensor based data acquisition, functional programming in Wolfram Language, instrument calibration, analysis, visualization, and machine learning. Here we discuss the various challenges and successes in this 15-week class.

Here’s a link to the presentation.

And here’s a version of it rendered from the Wolfram Cloud –