Machine Learning Lecture 1 | Empirical Risk Minimization & MSE | Probabilistic ML

Subtopic Split(in minutes elapsed) 0-6: Machine learning definition, motivating probabilistic approach to ML, Why Random variable is neither random nor variable. 6-10: Supervised Learning. 10-14: Iris Dataset. 15-17: Exploratory Data Analysis. 17-23: Learning a classifier, decision/nested decision boundary concept intuition. 24-32: Empirical Risk Minimization, Model fitting and Generalization. 32-39: Uncertainty in Machine Learning and how to model uncertainties. 39-43: SoftMax function intuition, equation. 43-48: Maximum Likelihood Estimation.