Online Learning and Online Algorithms

Online Learning and Online Algorithms

Online Learning and Online Algorithms are fundamental concepts in the field of machine learning and computer science. They provide a framework for making decisions and learning from data that is sequentially available, making them particularly useful in real-time applications and environments with non-stationary data.


Online Learning is a model of incremental learning where the learning algorithm makes predictions based on data as it arrives sequentially. Unlike batch learning, where the model is trained on the entire dataset at once, online learning updates the model incrementally with each new data point. This makes it highly adaptable to changes in the data over time.

Online Algorithms are algorithms that make decisions step-by-step, without knowledge of future inputs. They are designed to process their input piece-by-piece in a serial fashion, i.e., in the order that the input is fed to the algorithm, without having the entire input available from the start.


Online Learning and Online Algorithms are widely used in various fields such as finance, computer networks, mobile advertising, and more. They are particularly useful in scenarios where data is continuously generated and needs to be processed in real-time. For example, in stock market prediction, online learning algorithms can adapt to new trends and changes in the market.


  1. Adaptability: Online Learning and Online Algorithms can adapt to changes in the data over time, making them suitable for non-stationary environments.
  2. Efficiency: They can process data as it arrives, reducing the need for storage and allowing for real-time decision making.
  3. Scalability: They are well-suited to large-scale problems, as they do not require the entire dataset to be loaded into memory.


  1. Sensitivity to Noise: Online Learning and Online Algorithms can be sensitive to noise and outliers, as each data point can significantly influence the model.
  2. Dependence on Initial Conditions: The performance of these algorithms can be heavily influenced by their initial state or parameters.
  • Reinforcement Learning: A type of online learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties.
  • Streaming Algorithms: A type of online algorithm designed to process large streams of data, often used in big data applications.

Further Reading

  1. Online Learning: A Comprehensive Survey
  2. Online Algorithms in High-dimensional Prediction & Resource Allocation

This glossary entry is part of a series on Machine Learning and Data Science concepts, aimed at helping data scientists and researchers understand these key ideas.