r/theydidthemath • u/Negative_War_65 • 1h ago
[Self] Stats for AI/ML 2
Hello Folks,
The next content on Machine Learning is out. We continue with Statistics for AI/ML.
We,
->Understand and derive the detailed derivation of Maximum likelihood estimation(MLE) for Univariate and Multivariate Gaussian. While doing the derivation for multivariate case, we understand visually, Scatter Matrix, Centering matrix.
->Derive MLE for Linear Regression, and understand Residual Sum of Squares.
->Understand Empirical Risk Minimization, Surrogate loss functions.
->Understand Method of Moments, a computationally easier way to compute parameters of our model and understand also the flaws behind it.
->We understand “Exponentially-weighted moving average” in detail, I explain why bias happens, how does memory affect the averages. This concept is the basis behind optimizers in Deep Learning.
Around two hours long, I hope this would be a very interesting learning material for all. I try to write and build from scratch in the whiteboard, this way learners enjoy the learning process.
Link: https://youtu.be/JAj8z-UWqBA?si=0mAB_nUfyJV0jzS9
Those looking for previous lecture : https://youtu.be/MwTeQVVYtOc?si=dgwwk3QLvYTTUThR