Probabilistic Machine Learning | Lecture 1 | Empirical Risk & MLE

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.

Видео Probabilistic Machine Learning | Lecture 1 | Empirical Risk & MLE канала Machine Learning with Aayush