Intro

What is Machine Learning?

Fundamental Concepts in Machine Learning

Training: Model, Loss and Optimization

Validation and Testing and Deployment

Supervised, Unsupervised and Semi-supervised Learning

Overfitting and Underfitting

Building Intuitions in Neural Network

From Perceptron to Multi-Layer Perceptron

Neural Network: A Layered Approximator

Neural Network as Folding Process

Neural Network as Logic Gates

Neural Network as Template Matching

Why Deep Neural Network?

Numerical Optimization

Simple Optimization Problems

Iterative Optimization

Loss Function and Loss Surface