These are some machine learning notes, thoughts, tips & tricks that I’ve collected throughout the years of my studies. Some of them are from Introduction to Machine Learning and Neural Networks and Deep Learning taken at UCLA. Others are from various online courses such as Stanford’s CS231n, or books such as the deep learning book.

These notes represent my own understanding of the material and are likely to contain some errors. If you find any errors, inaccuracies, or points that could be clarified better, please send me a pull request or contact me!

#### Linear and Logistic Regression

- Probabalistic Interpretation for Linear Regression & Gradient Descent
- Linear & Logistic Regression, Convexity, Hessian Matrices, and closed-form solutions
- Regularized (ridge) linear regression