Intro

What is Machine Learning?

Fundamental Concepts in Machine Learning

Training: Model, Loss and Optimization

Validation and Testing and Deployment

Supervised Learning

Unsupervised Learning

Semi-Supervised Learning

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?

Prerequisite

Computational Graph

Numerical Optimization