What is Machine Learning?
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Machine learning is a computational approach that enables systems to improve their performance on tasks by learning from data. It is especially useful in neurotechnology, where vast amounts of brain-related data are gathered through techniques like electroencephalography (EEG), magnetoencephalography (MEG), and functional MRI (fMRI). These datasets contain patterns that reflect neural activity, which machine learning models can analyze to extract valuable insights.
Supervised vs. Unsupervised Learning
In the realm of machine learning, two primary learning paradigms are widely used: supervised learning and unsupervised learning.
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- Supervised Learning: In this approach, the model is trained on labeled data, meaning that each input comes with an associated output (or label). In neurotechnology, supervised learning is used for tasks like classifying brain states (e.g., predicting whether a person is awake or asleep based on EEG data). The algorithm learns to map input features (e.g., brain signals) to the correct labels (e.g., sleep stage) by minimizing prediction errors.
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- Unsupervised Learning: Unlike supervised learning, unsupervised learning deals with unlabeled data. The goal here is to uncover hidden patterns or group similar data points together. In neurotechnology, unsupervised learning can be applied to identify clusters of neurons with similar activity patterns or discover new brain states without predefined labels.