Machine Learning Blogs
We considered the problem of overfitting as model complexity increase in the prior post. Now we look at one way to control for this problem: regularization. The basic idea is to penalize each the model, essentially saying that we don't entirely believe the fit that falls out of our optimization. Since we are fitting to [...]
Data analysis is part science, part art. It is part algorithm and part heuristic. Of the various approaches to data analysis, machine learning falls more on the side of purely algorithmic, but even here we have many decisions to make which don't have well-defined answers (e.g. which learning algorithm to use, how to divide the [...]
The initial lectures in Stanford CS229a were concerned with regression problems where the predicted value was a continuous number. Another class of problems is concerned with discrete problems, where values are divided into groups (e.g. on or off; red, green, or blue). This builds on all the material from the previous linear regression lectures. The [...]
%20=%20%5Ctheta_0%20+%20%5Ctheta_1%20x_1%20+%20%5Ctheta_2%20x_2%20+%20...&bg=ffffff&%23038;fg=000000&%23038;s=0)
The next set of lectures in CS229 covers "Linear Regression with Multiple Variables", also known as Multivariate Regression. This builds on the univariate linear regression material and results in a more general procedure. As part of this, Professor Ng also provides more guidance on how to use Gradient Descent, and introduces the most widely used [...]
Machine learning makes extensive usage of linear algebra, probability, and calculus. CS229 reviews basic linear algebra early on. If you're new to linear algebra, it's certainly worth spending time on; I use it extensively in my professional life. I might expand on this subject more over time, but for now I would just highlight a [...]
