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103 lines
4.6 KiB
Markdown
103 lines
4.6 KiB
Markdown
# Machine learning
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Branch of [AI] focusing on developing models and algorithms that can learn patterns from data without being explicitly
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programmed for every task, and subsequently make accurate inferences about new data.
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It is a pattern recognition ability that enables models to make decisions or predictions without explicit, hard-coded
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instructions.
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## Table of contents <!-- omit in toc -->
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1. [TL;DR](#tldr)
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1. [Further readings](#further-readings)
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1. [Sources](#sources)
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## TL;DR
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All machine learning is AI, but not all AI is machine learning.
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Rules-based models become increasingly brittle the more data is added to them.<br/>
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They require accurate, universal criteria to define the results they need to achieve. This is not scalable.
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ML models operate by a logic that is learned through experience, and not explicitly programmed into them.<br/>
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They train by analyzing data and predicting the next result; prediction errors are calculated, and the algorithm is
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adjusted to reduce the possibility of errors.<br/>
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The training process is repeated until the model is accurate.
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ML works through mathematical logic. Relevant characteristics (A.K.A. _features_) of each data point **must** be
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expressed numerically, so that the data can be fed into the mathematical algorithm that will _learn_ to map a given
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input to the desired output.
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ML is mainly divided into the following types:
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- _Supervised_ learning.
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Models learn from _labelled_ data. Every input has a corresponding _correct_ output.<br/>
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Models make predictions, compare those with the true outputs, and adjust themselves to reduce errors and improve
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accuracy over time.<br/>
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The goal is to train to make accurate predictions on new, unseen data.
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- _Unsupervised_ learning.
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Models work **without** labelled data.<br/>
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They learn patterns on their own by grouping similar data points or finding hidden structures without human
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intervention.<br/>
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Helps identify hidden patterns in data. Useful for grouping, compression and anomaly detection.<br/>
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Used for tasks like clustering, dimensionality reduction and Association Rule Learning.
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- _Reinforcement_ learning.
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Teaches agents to make decisions through trial and error to maximize cumulative rewards.<br/>
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Allows machines to learn by interacting with an environment and receiving feedback based on their actions. This
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feedback comes in the form of rewards or penalties.<br/>
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Agents use the feedback to optimize their decision-making over time.
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- _Semi-supervised_ learning.
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Hybrid machine learning approach using both supervised and unsupervised learning.<br/>
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Uses a small amount of labelled data, combined with a large amount of unlabelled data to train models.<br/>
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The goal is to learn a function that accurately predicts outputs based on inputs, like with supervised learning, but
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with much less labelled data.<br/>
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Particularly valuable when acquiring labelled data is expensive or time-consuming, yet unlabelled data is plentiful
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and easy to collect.
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- _Self-supervised_ learning.
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Subset of unsupervised learning.<br/>
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Models train using data that does not have any labels or answers provided. Instead of needing people to label the
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data, the models themselves find patterns and create their own labels from the data automatically.<br/>
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Especially useful when there is a lot of data, but only a small part of it is labelled or labelling the data would
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take a lot of time and effort.
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_Deep learning_ has emerged as the state-of-the-art AI model architecture across nearly every domain.<br/>
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It relies on distributed _networks_ of mathematical operations providing the ability to learn intricate nuances of very
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complex data.<br/>
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It requires very large amounts of data and computational resources.
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## Further readings
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### Sources
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- geeksforgeeks.com's [Machine Learning Tutorial][geeksforgeeks / machine learning tutorial]
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- IBM's [What is machine learning?][ibm / what is machine learning?]
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- Oracle's [What is machine learning?][oracle / what is machine learning?]
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- [Machine learning, explained]
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[AI]: README.md
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[geeksforgeeks / Machine Learning Tutorial]: https://www.geeksforgeeks.org/machine-learning/
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[IBM / What is machine learning?]: https://www.ibm.com/think/topics/machine-learning
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[Oracle / What is machine learning?]: https://www.oracle.com/artificial-intelligence/machine-learning/what-is-machine-learning/
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[Machine learning, explained]: https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
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