2. 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

3. Linear Algebra Review
Matrices and Vectors
Addition and Scalar Multiplication
Matrix Vector Multiplication
Matrix Matrix Multiplication
Matrix Multiplication Properties
Inverse and Transpose

4. 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

5. Octave/Matlab Tutorial
Basic Operations
Moving Data Around
Computing on Data
Plotting Data
Control Statements: for, while, if statement
Vectorization

6. Logistic Regression
Classification
Hypothesis Representation
Decision Boundary
Cost Function
Simplified Cost Function and Gradient Descent
Advanced Optimization
Multiclass Classification: One-vs-all

7. Regularization
The Problem of Overfitting
Cost Function
Regularized Linear Regression
Regularized Logistic Regression

8. 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

9. Neural Networks: Learning
Cost Function
Backpropagation Algorithm
Backpropagation Intuition
Implementation Note: Unrolling Parameters
Gradient Checking
Random Initialization
Putting It Together
Autonomous Driving

10. 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

11. 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

12. Support Vector Machines
Optimization Objective
Large Margin Intuition
Mathematics Behind Large Margin Classification
Kernels I
Kernels II
Using An SVM

13. Unsupervised Learning
Unsupervised Learning: Introduction
K-Means Algorithm
Optimization Objective
Random Initialization
Choosing the Number of Clusters

14. 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

15. 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

16. Recommender Systems
Problem Formulation
Content Based Recommendations
Collaborative Filtering
Collaborative Filtering Algorithm
Vectorization: Low Rank Matrix Factorization
Implementational Detail: Mean Normalization

17. 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

18. 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