Machine Learning and Data Mining

“I don’t care if these parameters don’t mean anything to you – they don’t mean anything to me either.”

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Prof:  Dr. Michael Gelbart

Very affable professor. He is keen on his lectures (and even Piazza) not to be engulfed in sub-optimal questions, though, so the lectures are very well scheduled and structured. His background is in Biophysics which is pretty cool, as he will occasionally mention an application or connection to the physics that govern amoeba. He is very concerned about not contributing negatively to students’ mental health, often sparking heated Piazza discussions. Every now and then he will also drop some absolute gems such as:

“It’s easy to see that this is a good idea, but it takes a while longer to realize that it’s not a horrible idea”

“There’s a lot more to your loss function than just your GPA”

“Most of us already find it hard to find our way in this three-dimensional world”

“The mindset you should have right now is ‘I’m awake'”

“I’m a bit afraid to try this, but… Whatever”

“I was so much happier living a happy life, predicting the mean every time”

[Credits to Philip Haupt for recording these XD]

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Difficulty

This course covers a LOT of material. The assignments are also very time-consuming. The actual material varies from trivial facts about parabolas to quite complex gradient calculations involving matrices. That said, the midterms and finals do not go very deep into the material. Ultimately the average for the course was an A and I don’t think there was any scaling.

Key Concepts

Classification

Regression

Clustering

Dimensionality Reduction

Loss Functions

Regularization

Fundamental Trade-off

Neural Networks

Hard Concepts

Convolutional Neural Networks: Not really sure how they worked. Thankfully, no question was asked about it on the final exam.

Kernel Regression and Kernel Trick: Not immediately obvious how these things work and inter-related. Possibly useful to think of the underly mathematics of inner products.

Conclusion

A broad introduction to the hot-topic that is Machine Learning. Enjoyable though time-consuming class.