In a series of few short videos, we will go over a general, non-technical introduction to Machine Learning (ML). We will define and explain a few fundamental concepts in ML, including overfitting, cross-validation, VC-dimension, regularization and others. This module is designed to help a general audience, including newcomers. My hope is that this lesson aids in understanding what applications are best suited for ML, provides intuition behind ML algorithms and conveys the importance of ML in today’s world.

Machine Learning Intro 1: ML basic framework, Supervised learning, and example application.

Machine Learning Intro 2: Classification vs regression, AI, supervised vs unsupervised learning, clustering, and ML for finance.

Machine Learning Intro 3: Linear regression, RSS, and Gradient Descent

Machine Learning Intro 4: Generalization Error, Train vs Test Sets, and Cross Validation.

Machine Learning Intro 5: Overfitting, underfitting, and VC dimension.

Machine Learning Intro 6: Bias-Variance Trade-Off, Learning Curves, and Fitting Graphs.

Machine Learning Intro 7: Decision Trees, Support Vector Machines (SVM), Neural Networks, and Reinforcement Learning.

Acknowledgement

Special thanks to Santiago Correa Cardona for his great help in preparing the slides.