Learning Algorithm

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What Is a Learning Algorithm?

A learning algorithm is a set of instructions that determines how a machine learning (ML) model learns from data. Learning algorithms allow machine learning models to independently improve the way they create outputs to complete a specific task or type of task. Essentially, learning algorithms train the model, and the model generates outputs.

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What Is a Learning Algorithm?

Key Takeaways

  • Learning algorithms provide the instructions that machine learning models use to learn from data.
  • Generally, the core logic of a learning algorithm remains fixed once written.
  • Some types of learning algorithms are optimized for specific tasks, while others can be used for more than one type of task.
  • Selecting the right algorithm(s) for a machine learning model depends on the task, the type of data that’s being used for training, and the available computational resources.
  • Unlike other types of algorithms, learning algorithms don’t produce a direct output – they simply guide the learning process for machine learning models. Once trained, the model (not the algorithm) produces outputs when presented with new data.

The Role of Learning Algorithms in Artificial Intelligence

In artificial intelligence (AI), a learning algorithm guides the process that a machine learning model uses to discover patterns and relationships in training data. This type of algorithm determines how the model uses training data to make accurate predictions or decisions about new, unseen data.

How Learning Algorithms Work

Learning algorithms are instructions that a machine learning model uses during training. Generally, the core logic of a learning algorithm remains fixed once written.

Like any computer code, however, learning algorithms can have bugs or inefficiencies. If this happens, developers may revise the algorithm to improve the model’s performance or optimize the learning algorithm for specific hardware.

Developers may also adjust the settings that control the learning process for a specific training dataset by tuning hyperparameters. A hyperparameter is a special kind of AI parameter that is determined before the training process begins.

Hyperparameter tuning, also known as hyperparameter optimization, is important because the same learning algorithm may work differently with different types of training data and machine learning models. For example, a neural network trained on small datasets, may need a higher dropout rate to help reduce overfitting.

Types of Machine Learning Algorithms

Machine learning algorithms can be categorized by whether they are intended for general use or specific purposes.

General-purpose learning algorithms are versatile and can be adapted for a wide variety of purposes across different domains. In contrast, specialized learning algorithms are optimized for domain-specific data structures or hardware.

General Purpose vs. Specialized Learning Algorithms

General purpose learning algorithms

Algorithm Use cases
Linear regression Used to train models on how to identify continuous target variables.
Logistic regression Used to train models for classification tasks.
Decision trees Can be used to train models for regression tasks.
Random forest Used to train models for ensemble regression and classification tasks.
Support Vector Machines (SVMs) Used to train models in high-dimensional spaces for classification/regression tasks.
k-Nearest Neighbors (k-NN) Used to train models on how to use data proximity for classification and regression tasks.
Stochastic Gradient Descent (SGD) Optimization method for training models.

Specialized learning algorithms

Algorithm Use cases
Convolutional Neural Networks (CNNs) Used to train models for image recognition and computer vision tasks.
Recurrent Neural Networks (RNNs) Used to train models how to generate sequential data
Transformers Used to train models for tasks involving sequential data and attention-based processing, such as natural language processing and image recognition.
Generative Adversarial Networks (GANs) Used to train models on how to complete generative AI tasks.
Autoencoders Used to train models on how to detect anomalies.
Deep Q-Networks (DQN) Used to train agents how to use reinforcement learning.
YOLO (You Only Look Once) Can be used to train models on how to detect objects in real-time.

Supervised vs. Unsupervised vs. Semi-Supervised & Reinforcement Learning Algorithms

Machine learning algorithms can also be classified by the type of data they use during training to guide machine learning models:

  • Supervised learning algorithms use labeled input-output pairs.
  • Unsupervised learning algorithms use unlabeled data.
  • Semi-supervised learning algorithms use a limited number of labeled input-output pairs and a lot of unlabeled data.
  • Reinforcement learning algorithms interact with the environment and use feedback to guide learning models. The feedback can be determined statistically or be provided directly by humans.

Learning Algorithm Examples

Here are some examples of popular supervised, unsupervised, semi-supervised, and reinforcement learning algorithms.

Supervised learning algorithms

Unsupervised learning algorithms:

Semi-supervised learning algorithms:

Reinforcement learning algorithms:

Machine Learning Algorithms Pros and Cons

Machine learning algorithms, like other types of algorithms used in computing, have both pros and cons. Their effectiveness depends on selecting the right type of learning algorithm(s) for the machine learning model being trained:

Pros

  • Enable models to find patterns in training data
  • Help machine learning models automate tasks that would be difficult to program manually
  • Ensemble methods boost performance by combining multiple algorithms

Cons

  • Can be hard to explain in how they guide predictions
  • Deep learning can require significant computational power and time

The Bottom Line

The definition of learning algorithm is often confused with the definition of learning model, particularly by individuals who are new to machine learning and artificial intelligence. Some of the confusion is tied to the way traditional algorithms are procedural and produce direct outputs when given inputs.

It’s important to understand that in AI and machine learning, algorithms guide the training process for machine learning models, and models produce outputs based on what was learned during training.

FAQs

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Margaret Rouse
Technology Expert
Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.