Day 1 – AI Foundations

3. Understanding AI, Machine Learning and Deep Learning

Day 1 · AI/ML Fundamentals

Understanding AI, Machine Learning and Deep Learning

Learn how artificial intelligence, machine learning and deep learning are related, how they differ, and how they can support weather and climate applications.

Learning objectives

By the end of this chapter, you should be able to:

Explain artificial intelligence in simple terms.
Describe machine learning as a branch of artificial intelligence.
Explain how deep learning relates to machine learning.
Differentiate training, validation, testing and inference.
Recognise examples of AI methods used in weather and climate services.

How the three concepts are related

Artificial intelligence is the broadest concept. Machine learning is one approach within artificial intelligence. Deep learning is a family of machine-learning methods based on neural networks with multiple processing layers.

Artificial Intelligence The broad field of systems that perform tasks involving prediction, pattern recognition, reasoning or decision support.

Machine Learning A branch of AI in which models learn patterns from examples in data.

Deep Learning A type of machine learning based on multilayer neural networks.

Artificial intelligence

Artificial intelligence refers to computer systems designed to perform tasks that normally require human intelligence. These tasks may include recognising patterns, interpreting images, processing language, generating predictions and supporting decisions.

AI does not refer to one single method. It includes rule-based systems, optimisation, expert systems, machine learning, neural networks and other computational approaches.

Weather example A computer system analyses satellite images and identifies rapidly developing convective clouds that may produce severe weather.

Machine learning

Machine learning is a branch of artificial intelligence in which a model learns relationships from historical examples instead of relying only on rules written manually.

During training, the model examines input data and known outcomes. It estimates patterns that can later be used to make predictions for new cases.

Weather example A model learns from past numerical forecasts and station observations to correct systematic temperature forecast errors.

Deep learning

Deep learning is a type of machine learning based on artificial neural networks with multiple processing layers. These layers allow the model to learn increasingly complex representations of the data.

Deep-learning approaches are often used for large, complex datasets such as satellite imagery, radar sequences and gridded weather fields.

Weather example A neural network analyses a sequence of satellite images to estimate the movement and development of clouds and rainfall.

Common machine-learning approaches

Supervised learning The model learns from inputs paired with known answers, such as historical forecasts paired with observed temperatures.

Unsupervised learning The model searches for patterns or groups in data without being given predefined answers.

Reinforcement learning A system learns through repeated actions and feedback based on rewards or penalties.

Training, validation, testing and inference

Training The model learns relationships from historical examples.

Validation A separate dataset helps compare models and adjust settings.

Testing Independent data are used to estimate final model performance.

Inference The trained model is applied to new data to produce a prediction.

Avoid data leakage

Information from the test period must not be used during model training. Otherwise, the reported performance may appear better than the model’s true ability to work on unseen data.

Explore

Which concept best fits each example?

A system uses manually defined rules to issue a warning when rainfall exceeds a fixed threshold.

This is an example of a rule-based AI system. It does not necessarily use machine learning.

A model learns from historical forecasts and observations to predict forecast error.

This is machine learning, most likely supervised learning.

A multilayer neural network processes satellite images to predict rainfall.

This is deep learning because it uses a multilayer neural network.

A trained model receives today’s forecast data and produces tomorrow’s temperature prediction.

This is inference: the trained model is being applied to new data.

Quick self-check

Is every AI system based on machine learning?

No. AI also includes rule-based systems, expert systems, optimisation methods and other approaches.

Is every machine-learning model a deep-learning model?

No. Machine learning also includes linear regression, decision trees, random forests, gradient boosting and many other methods.

Why should test data be kept separate?

Separate test data provide a more realistic estimate of how the model will perform on examples it has not seen previously.

Does a more complex model always perform better?

No. A complex model may overfit, require more data and computing resources, and be harder to explain and maintain.