来源：原始 Jupyter Notebook。内容已转换为网页阅读格式；下载原文件。

  • 课程名称：Machine Learning
  • 课程来源：https://www.coursera.org/
  • 课程地址：https://www.coursera.org/learn/machine-learning
  • 讲师：Andrew Ng

Week 1

Introduction

Welcome to Machine Learning!

Welcome

What is Machine Learning?

Supervised Learning

Unsupervised Learning

Linear Regression with One Variable

Model Representation

Cost Function

Cost Function - Intuition I

Cost Function - Intuition II

Gradient Descent

Gradient Descent Intuition

Gradient Descent For Linear Regression

Linear Algebra Review

Matrices and Vectors

Addition and Scalar Multiplication

Matrix Vector Multiplication

Matrix Matrix Multiplication

Matrix Multiplication Properties

Inverse and Transpose

Week 2

Linear Regression with Multiple Variables

Multiple Features

Gradient Descent for Multiple Variables

Gradient Descent in Practice I - Feature Scaling

Gradient Descent in Practice II - Learning Rate

Features and Polynomial Regression

Normal Equation

Normal Equation Noninvertibility

Working on and Submitting Programming Assignments

Octave/Matlab Tutorial

Basic Operations

Moving Data Around

Computing on Data

Plotting Data

Control Statements: for, while, if statement

Vectorization

Week 3

Logistic Regression

Classification

Hypothesis Representation

Decision Boundary

Cost Function

Simplified Cost Function and Gradient Descent

Advanced Optimization

Multiclass Classification: One-vs-all

Regularization

The Problem of Overfitting

Cost Function

Regularized Linear Regression

Regularized Logistic Regression

Week 4

Neural Networks: Representation

Non-linear Hypotheses

Neurons and the Brain

Model Representation I

Model Representation II

Examples and Intuitions I

Examples and Intuitions II

Multiclass Classification

Week 5

Neural Networks: Learning

Cost Function

Backpropagation Algorithm

Backpropagation Intuition

Implementation Note: Unrolling Parameters

Gradient Checking

Random Initialization

Putting It Together

Autonomous Driving

Week 6

Advice for Applying Machine Learning

Deciding What to Try Next

Evaluating a Hypothesis

Model Selection and Train/Validation/Test Sets

Diagnosing Bias vs. Variance

Regularization and Bias/Variance

Learning Curves

Deciding What to Do Next Revisited

Machine Learning System Design

Prioritizing What to Work On

Error Analysis

Error Metrics for Skewed Classes

Trading Off Precision and Recall

Data For Machine Learning

Week 7

Support Vector Machines

Optimization Objective

Large Margin Intuition

Mathematics Behind Large Margin Classification

Kernels I

Kernels II

Using An SVM

Week 8

Unsupervised Learning

Unsupervised Learning: Introduction

K-Means Algorithm

Optimization Objective

Random Initialization

Choosing the Number of Clusters

Dimensionality Reduction

Motivation I: Data Compression

Motivation II: Visualization

Principal Component Analysis Problem Formulation

Principal Component Analysis Algorithm

Reconstruction from Compressed Representation

Choosing the Number of Principal Components

Advice for Applying PCA

Week 9

Anomaly Detection

Problem Motivation

Gaussian Distribution

Algorithm

Developing and Evaluating an Anomaly Detection System

Anomaly Detection vs. Supervised Learning

Choosing What Features to Use

Multivariate Gaussian Distribution

Anomaly Detection using the Multivariate Gaussian Distribution

Recommender Systems

Problem Formulation

Content Based Recommendations

Collaborative Filtering

Collaborative Filtering Algorithm

Vectorization: Low Rank Matrix Factorization

Implementational Detail: Mean Normalization

Week 10

Large Scale Machine Learning

Learning With Large Datasets

Stochastic Gradient Descent

Mini-Batch Gradient Descent

Stochastic Gradient Descent Convergence

Online Learning

Map Reduce and Data Parallelism

Week 11

Application Example: Photo OCR

Problem Description and Pipeline

Sliding Windows

Getting Lots of Data and Artificial Data

Ceiling Analysis: What Part of the Pipeline to Work on Next

Summary and Thank You