Photonics Dictionary

machine learning

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience or training. Instead of being explicitly programmed to perform a task, a machine learning system learns from data and examples.

The primary goal of machine learning is to develop models that can generalize patterns from data and make predictions or decisions without being explicitly programmed for every input scenario. There are three main types of machine learning:

Supervised learning: In supervised learning, the algorithm is trained on a labeled dataset, where the input data is paired with corresponding output labels. The algorithm learns to map the input data to the correct output by generalizing from the training examples. Common tasks include classification and regression.

Unsupervised learning: Unsupervised learning involves training algorithms on unlabeled data. The system tries to identify patterns or relationships within the data without explicit guidance on the output. Clustering and dimensionality reduction are common unsupervised learning tasks.

Reinforcement learning: Reinforcement learning involves training an agent to make decisions in an environment to achieve a goal. The agent receives feedback in the form of rewards or penalties based on its actions, allowing it to learn through trial and error.

Key concepts in machine learning include:

Features and labels: Features are the input variables used to make predictions, and labels are the outputs or outcomes that the model aims to predict.

Training and testing: Models are trained on a subset of the data (training set) and then evaluated on another subset (testing set) to assess their performance on new, unseen data.

Overfitting and underfitting: Overfitting occurs when a model learns the training data too well but fails to generalize to new data. Underfitting happens when a model is too simple to capture the underlying patterns in the data.

Algorithms: Machine learning algorithms, such as decision trees, support vector machines, neural networks, and many others, are used to learn patterns and relationships in data.

Machine learning finds applications in various domains, including image and speech recognition, natural language processing, recommendation systems, autonomous vehicles, healthcare, finance, and more. The field continues to evolve with advancements in algorithms, data availability, and computing power.

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