Step #2 ✅: Building core intuitions from each paper

I started by trying to understand the core intuitions and math behind each paper.

Going through the early machine learning papers on linear models, trees, optimization, and regularization, this process was fairly straightforward.

Each of these papers builds directly on top of the core idea of learning from data, and shows empirically the techniques that solved specific problems in classical ML. Assuming a strong fundamental understanding of statistics and optimization, they were mostly intuitive.

Specifically, the framework of thinking about each advancement in terms of how it affects bias, variance, and generalization was particularly effective.

The math behind some of the more classical algorithms like support vector machines, kernel methods, and probabilistic models was a bit more challenging at times, but aside from that, the supervised learning and ensemble learning sections were also intuitive.

Many of the advancements in modern machine learning after the classic algorithms are about better feature representation, stronger regularization, improved optimization, and scaling models to more complex data.

However, when I got to the probabilistic and unsupervised learning sections, I got hit with a completely new level of difficulty.

Getting through the papers for PCA, Gaussian Mixture Models, Hidden Markov Models, and Bayesian inference was brutal. Some of the ideas took me a few days to fully wrap my head around, especially when the math started involving latent variables, likelihoods, priors, and uncertainty.

Because these models are built around reasoning under uncertainty, hidden structure, and probability distributions, they deal with concepts far more abstract than the simpler supervised learning algorithms.

It was painful getting through this part, but felt great at the end when I was finally able to grasp the math.