LED Graduation Cap
Machine Learning Course
Over the summer of 2018 I had the honor to teach an Introduction to Machine Learning course for UF freshman engineering students.
Syllabus:
Objectives
The objectives for the course were to provide the students with an understanding of the following:
- Learn what Machine Learning is
- Learn the K-Nearest Neighbors (K-NN) algorithm
- Learn how to apply the K-Nearest Neighbors algorithm to real data
Class Overview
During the course, students learned how to program in python, what large dimensional data is, how to calculate large dimensional distances, and how to run experiments.
Competition
To finalyze the course, the students formed 8 teams of 5 to compete in a 2 week class competition. The competition was to label images of diffraction gradients using the team’s collected dataset and a provided K-NN code. During the competition the students constructed spectroscopes using gradient filter and provided iPads. The students then explored Weil hall, a 4 story engineering building, to collect their dataset of images of different light sources. In order to collect data the students needed to point their spectroscopes at different light sources and take a picture of the light defraction. Finally, each team labeled their data and ran the provided compilation script to form their dataset.
Competition Day
On the final day of the competition, the teams were given a test dataset and asked to label where the images were taken and what kind of light it was. To our surprise the teams did well and the winning team labeled 6/9 of the test data set correctly.
The students enjoyed the class and were happy to get exposure to machine learning and programming.
Class Materials Repository
If you would like to check out the class materials please check out my github repository.
Thanks,
James
2019
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