**01: Introduction**
- Introduction to the course
- What is machine learning?
- Supervised learning - introduction
- Unsupervised learning - introduction

**02: Regression Analysis and Gradient Descent**
- Linear Regression
- Linear regression - implementation (cost function)
- A deeper insight into the cost function - simplified cost function
- Gradient descent algorithm
- So no need to change alpha over time
- Linear regression with gradient descent

**03: Linear Algebra - review**
- Matrices - overview
- Vectors - overview
- Matrix manipulation
- Implementation/use
- Matrix multiplication properties
- Inverse and transpose operations

**04: Linear Regression with Multiple Variables**
- Linear regression with multiple features
- Gradient descent for multiple variables
- Gradient Decent in practice: 1 Feature Scaling
- Learning Rate a
- Features and polynomial regression
- Normal equation

**05: Logistic Regression**
- Classification
- Hypothesis representation
- Decision boundary
- Non-linear decision boundaries
- Cost function for logistic regression
- Simplified cost function and gradient descent
- Advanced optimization
- Multiclass classification problems

**06: Regularization**
- The problem of overfitting
- Cost function optimization for regularization
- Regularized linear regression
- Regularization with the normal equation
- Advanced optimization of regularized linear regression

**07: Neural Networks - Representation**
- Neural networks - Overview and summary
- Model representation 1
- Model representation II
- Neural network example - computing a complex, nonlinear function of the input
- Multiclass classification

**08: Neural Networks - Learning**
- Neural network cost functionx
- Summary of what's about to go down
- Back propagation algorithm
- Back propagation intuition
- Implementation notes - unrolling parameters (matrices)
- Gradient checking
- Random initialization
- Putting it all together

**09: Advice for applying machine learning techniques**
- Deciding what to try next
- Evaluating a hypothesis
- Model selection and training validation test sets
- Diagnosis - bias vs. variance
- Regularization and bias/variance
- Learning curves

**10: Machine Learning System Design**
- Machine learning systems design
- Prioritizing what to work on - spam classification example
- Error metrics for skewed analysis
- Trading off precision and recall
- Data for machine learning

**11: Support Vector Machines**
- Support Vector Machine (SVM) - Optimization objective
- Large margin intuition
- Large margin classification mathematics (optional)
- Kernels - 1: Adapting SVM to non-linear classifiers
- Kernels II

**12: Clustering**
- Unsupervised learning - introduction
- K-means algorithm
- K means optimization objective
- How do we choose the number of clusters?

**13: Dimensionality Reduction**
- Motivation 1: Data compression
- Motivation 2: Visualization
- Principle Component Analysis (PCA): Problem Formulation
- PCA Algorithm
- Reconstruction from Compressed Representation
- Choosing the number of Principle Components
- Advice for Applying PCA

**14: Anomaly Detection**
- Anomaly detection - problem motivation
- The Gaussian distribution (optional)
- Anomaly detection algorithm
- Developing and evaluating and anomaly detection system
- Anomaly detection vs. supervised learning
- Choosing features to use
- Multivariate Gaussian distribution
- Applying multivariate Gaussian distribution to anomaly detection

**15: Recommender Systems**
- Recommender systems - introduction
- Content based recommendation
- Collaborative filtering - overview
- Collaborative filtering Algorithm
- Vectorization: Low rank matrix factorization
- Implementation detail: Mean Normalization

**16: 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

**17: Application Example - Photo OCR**
- Problem description and pipeline
- Sliding window image analysis

**18: Course Summary**