- Supervised Machine Learning
- Unsupervised Machine Learning
- Self-Supervised Machine Learning
Classical programming uses programs (algorithms) to create results:
Traditional Computing
Data + Computer Algorithm = Result
Machine Learning uses results to create programs (algorithms):
Machine Learning
Data + Result = Computer Algorithm
Machine Learning
Machine Learning is often considered equivalent with Artificial Intelligence.
This is not correct. Machine learning is a subset of Artificial Intelligence.
Machine Learning is a discipline of AI that uses data to teach machines.
“Machine Learning is a field of study that gives computers the ability to learn without being programmed.”
Arthur Samuel (1959)
Supervised Learning
Supervised learning uses labeled data (data with known answers) to train algoritms to:
- Classify Data
- Predict Outcomes
Supervised learning can classify data like “What is spam in an e-mail”, based on known spam examples.
Supervised learning can predict outcomes like like predicting what kind of video you like, based on videos you have played.
Unsupervised Learning
Unsupervised learning is used to predict undefined relationships like meaningful patterns in data.
It is about creating computer algorithms than can improve themselves.
It is expected that machine learning will shift to unsupervised learning to allow programmers to solve problems without creating models.
Self-Supervised Learning
Self-supervised learning is similar to unsupervised learning because both work with data without human added labels.
The difference is that unsupervised learning uses clustering, grouping, and dimensionality reduction, while self-supervised learning draw its own conclusions for regression and classification tasks.
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