Day 1

The day started with a brief gander at the intern handbook and a tour around the Carlson building, then us interns went down to the Red Barn for some team building. We got to learn each other's names before inspecting many ropes, building PVC pipe structures of questionable integrity, unsafely flipping magic carpets, and more. We're a pretty quiet group, which made the team building a bit difficult, but we got to know each other nonetheless. Ending with some final thoughts, we wandered our way back to the Carlson building for lunch, which was provided for us (thanks Mr. Pow!).

After lunch we got to meet with our respective research groups. When we got to the lab, Ron and Dr. Kanan talked to Nate (my fellow intern) and I about college, research, and the internship in general. Ron went through all of the topics we'd be covering through tutorials and readings throughout the first week, just to give us an idea about our focus. I'm really glad he took the time to put together a curriculum for us; there's a lot to learn. Hyperspectral images, hyperparameters, feature extractors, support vector machines, activation functions, dimensionality reduction, convolutional autoencoders; the list goes on. The amount of jargon in the field of machine learning is unreal, but Ron did a great job at explaining the concepts at a less technical level. We wrapped up the day by setting up our accounts on the server, SSHing into them, and decking them out with Anaconda and some libraries. I was able to start work on the first tutorial assignment, which I will be continuing tomorrow.

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