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• Machine Learning
• Supervised learning
• Unsupervised learning
• Model and cost function
• Parameter learning
• Linear algebra
• Linear regression
• Linear regression with multiple variables
• Gradient descent
• Features and polynomial regression
• Computing parameters analytically
• Logistic regression
• Classification and representation
• Hypothesis representation
• Decision boundary
• Multi class classification
• Regularisation
• Neural networks
• Non linear hypothesis
• Back propagation
• Evaluating a learning algorithm
• Advice for applying machine learning
• Bias vs variance
• Learning curves
• Building a spam classifier
• Machine learning system design
• Handling skewed data
• Using large data sets
• Support vector machines
• Large margin classification
• Kernels
• SVM in practice
• Clustering
• Dimensionality reduction
• Motivation
• Principal component analysis
• Applying PCA
• Anomaly detection
• Density estimation
• Gaussian distribution
• Building an anomaly detection system
• Recommender systems
• Predicting movie ratings
• Collaborative filtering
• Low rank matrix factorisation
• Large scale machine learning
• Gradient descent with large data sets
• Advanced topics
• Online learning
• Map reduction and parallelism
• Photo OCR