1. Introduction
1.1 What is Online Learning?
1.2 Tasks and Applications
1.3 Taxonomy
1.4 Related Work and Further Reading

2. Problem Formulations and Related Theory
2.1 Problem Settings
2.2 Statistical Learning Theory
2.3 Convex Optimization Theory
2.4 Game Theory

3. Online Supervised Learning
3.1 Overview
3.2 First-order Online Learning
3.3 Second-Order Online Learning
3.4 Prediction with Expert Advice
3.5 Online Learning with Regularization
3.6 Online Learning with Kernels
3.7 Online to Batch Conversion

4. Applied Online Learning for Supervised Learning
4.1 Overview
4.2 Cost-Sensitive Online Learning
4.3 Online Multi-task Learning
4.4 Online Multi-view Learning
4.5 Online Transfer Learning
4.6 Online Metric Learning
4.7 Online Collaborative Filtering
4.8 Online Learning to Rank
4.9 Distributed Online Learning
4.10 Online Learning with Neural Networks
4.11 Online Portfolio Selection

5. Bandit Online Learning
5.1 Overview
5.2 Stochastic Bandits
5.3 Adversarial Bandits
5.4 Contextual Bandits
5.5 Other Bandit Variants

6. Online Active Learning
6.1 Overview
6.2 Selective Sampling Algorithms
6.3 Online Active Learning with Expert Advice

7. Online Semi-supervised Learning
7.1 Overview
7.2 Online Manifold Regularization
7.3 Transductive Online Learning

8. Online Unsupervised Learning
8.1 Overview
8.2 Online Clustering
8.3 Online Dimension Reduction
8.4 Online Density Estimation
8.5 Online Anomaly Detection

9. Related Areas and Other Terminologies
9.1 Overview
9.2 Incremental Learning
9.3 Sequential Learning
9.4 Stochastic Learning
9.5 Adaptive Learning
9.6 Interactive Learning
9.7 Reinforcement Learning
9.8 Continual Learning

10. Conclusions
10.1 Concluding Remarks
10.2 Future Directions