AI
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I took this intro to AI class in the Fall of 2020. Although it was an introduction course, I found many of the things we learned to be very practical. We went over topics 
such as optimization algorithms, supervised learning, and unsupervised learning.


Algorithms
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This course was a more advanced continuation of my previous c++ courses. We spent time studying more advanced algorithms and data structures; some of which we had heard of 
before but had not gotten any practical experience with. We discussed algorithm analysis, hash tables, trees, search algorithms, inheritance, and other similar topics.

Assembly
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Although the folder is titled assembly, this class was technically called "Assembly and Organization". Here we spent 90% of our time working with a program called Logisim
to create breadboard like circuits. All of these components were eventually pieced together to create a relatively simple computer. The assembly code here was what
we ran with our relatively simple computers.

Discrete
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I took this course at the same time as my Algorithms and Data structures course. This class just ended up mirroring many of the topics learned in ADS but with a more 
conceptual, mathematical approach. We covered additional topics such as matrix algebra and Turing tests.

Neural Networks
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Even though this course was technically an introduction to neural networks we really spent most of out time working practically with networks. The semester long project
can be found under the organization tab but to summarize, we built a CNN that was made using images gathered with a selenium web scraper. The dataset was small but the 
model had up to a 87% accuracy on new images.