课时1：Welcome

课时2：What is Machine Learning

课时3：Supervised Learning

课时4：Unsupervised Learning

课时5：Model Representation

课时6：Cost Function

课时7：Cost Function - Intuition I

课时8：Cost Function - Intuition II

课时9：Gradient Descent

课时10：Gradient Descent Intuition

课时11：Gradient Descent For Linear Regression

课时12：What-'s Next

课时13：Matrices and Vectors

课时14：Addition and Scalar Multiplication

课时15：Matrix Vector Multiplication

课时16：Matrix Matrix Multiplication

课时17：Matrix Multiplication Properties

课时18：Inverse and Transpose

课时19：Multiple Features

课时20：Gradient Descent for Multiple Variables

课时21：Gradient Descent in Practice I - Feature Scaling

课时22：Gradient Descent in Practice II - Learning Rate

课时23：Features and Polynomial Regression

课时24：Normal Equation

课时25：Normal Equation Noninvertibility (Optional)

课时26：Basic Operations

课时27：Moving Data Around

课时28：Computing on Data

课时29：Plotting Data

课时30：Control Statements- for, while, if statements

课时31：Vectorization

课时32：Working on and Submitting Programming Exercises

课时33：Classification

课时34：Hypothesis Representation

课时35：Decision Boundary

课时36：Cost Function

课时37：Simplified Cost Function and Gradient Descent

课时38：Advanced Optimization

课时39：Multiclass Classification- One-vs-all

课时40：The Problem of Overfitting

课时41：Cost Function

课时42：Regularized Linear Regression

课时43：Regularized Logistic Regression

课时44：Non-linear Hypotheses

课时45：Neurons and the Brain

课时46：Model Representation I

课时47：Model Representation II

课时48：Examples and Intuitions I

课时49：Examples and Intuitions II

课时50：Multiclass Classification

课时51：Cost Function

课时52：Backpropagation Algorithm

课时53：Backpropagation Intuition

课时54：Implementation Note- Unrolling Parameters

课时55：Gradient Checking

课时56：Random Initialization

课时57：Putting It Together

课时58：Autonomous Driving

课时59：Deciding What to Try Next

课时60：Evaluating a Hypothesis

课时61：Model Selection and Train-Validation-Test Sets

课时62：Diagnosing Bias vs. Variance

课时63：Regularization and Bias-Variance

课时64：Learning Curves

课时65：Deciding What to Do Next Revisited

课时66：Prioritizing What to Work On

课时67：Error Analysis

课时68：Error Metrics for Skewed Classes

课时69：Trading Off Precision and Recall

课时70：Data For Machine Learning

课时71：Optimization Objective

课时72：Large Margin Intuition

课时73：Mathematics Behind Large Margin Classification (Optional)

课时74：Kernels I

课时75：Kernels II

课时76：Using An SVM

课时77：Unsupervised Learning- Introduction

课时78：K-Means Algorithm

课时79：Optimization Objective

课时80：Random Initialization

课时81：Choosing the Number of Clusters

课时82：Motivation I- Data Compression

课时83：Motivation II- Visualization

课时84：Principal Component Analysis Problem Formulation

课时85：Principal Component Analysis Algorithm

课时86：Choosing the Number of Principal Components

课时87：Reconstruction from Compressed Representation

课时88：Advice for Applying PCA

课时89：Problem Motivation

课时90：Gaussian Distribution

课时91：Algorithm

课时92：Developing and Evaluating an Anomaly Detection System

课时93：Anomaly Detection vs. Supervised Learning

课时94：Choosing What Features to Use

课时95：Multivariate Gaussian Distribution (Optional)

课时96：Anomaly Detection using the Multivariate Gaussian Distribution (Optional)

课时97：Problem Formulation

课时98：Content Based Recommendations

课时99：Collaborative Filtering

课时100：Collaborative Filtering Algorithm

课时101：Vectorization- Low Rank Matrix Factorization

课时102：Implementational Detail- Mean Normalization

课时103：Learning With Large Datasets

课时104：Stochastic Gradient Descent

课时105：Mini-Batch Gradient Descent

课时106：Stochastic Gradient Descent Convergence

课时107：Online Learning

课时108：Map Reduce and Data Parallelism

课时109：Problem Description and Pipeline

课时110：Sliding Windows

课时111：Getting Lots of Data and Artificial Data

课时112：Ceiling Analysis- What Part of the Pipeline to Work on Next

课时113：Summary and Thank You