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Most of the coding I am doing nowadays is for my MS in Computer Science. Per the Georgia Tech honor code, I cannot post any of the projects to my Github, so my commits, for now, will be rare. Instead, I will blog about each class I take.

Spring 2022 Knowledge-Based Artificial Intelligence (KBAI)

Perhaps the best class I could have possibly started with; it was well-organized, engaging, and just the right difficulty. This class requires introspection into human cognition - How do we solve problems and learn? I am not sure I ever considered that before this class. Can we map the way we solve problems onto a machine? Turns out, yes. Sort of. Knowledge representation is extremely complicated in the human brain, and making these connections explicit in computer code is no small task. Sometimes it feels like that AI really is…is a bunch of IF statements!

The idea behind KBAI is that we need Agents that can function with limited knowledge of the world, similar to human cognition. This kind of machine learning actually predates the more popular statistics-based machine learning that has exploded in recent years because of the immense improvements in computing power and access to oodles of training data. However, knowledge representation is still extremely powerful in the field of NLP (one of the modules of this course).

The capstone project was to construct an Agent that could solve Raven’s Progressive Matrices, a fairly old intelligence test that is supposedly culture-independent. The idea is to recognize patterns in an image and pick the answer that completes the either 2x2 or 3x3 matrix. When we first got the prompt at the beginning of the course, I was sure I would fail miserably. But that’s the nice thing about captstone projects, at the end it really proves you learn something. My Agent could solve somewhere around 65/95 problems, classifying it as moderately intelligent. Or maybe it’s me that is moderately intelligent for programming it. Who knows.

Summer 2022 Machine Learning for Trading (ML4T)
Under construction.