A Brief Introduction to Machine Learning ( From Physics)

Machine learning is everywhere but the theory behind it is relatively easy. How do we discover models from data Machine learning automates what physicists have done for centuries. I introduce basic machine learning concepts like linear predictors, feature selection, the loss function, the stochastic gradient descent, classification, over and underfitting, the hypothesis class, hyperparameters, and the general machine learning workflow. , ,,, CHAPTERS, ,,, 00:00 From physics to machine learning 11:05 Linear predictors 18:15 Loss function and gradient descent 25:30 Classification 35:10 Overfitting and underfitting 42:58 Feature selection 47:10 Summary and ML workflow