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 Those looking for previous lecture : https://youtu.be/MwTeQVVYtOc submitted by /u/Negative_War_65
Originally posted by u/Negative_War_65 on r/ArtificialInteligence