Machine Learning (ML)
- Supervised Machine Learning
- Unsupervised Machine Learning
- Self-Supervised Machine Learning
Classical programming uses programs (algorithms) to create results:
Data + Computer Algorithm = Result
Machine Learning uses results to create programs (algorithms):
Data + Result = Computer Algorithm
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)
Intelligent Decision Formula
- Save the result of all actions
- Simulate all possible outcomes
- Compare the new action with the old ones
- Check if the new action is good or bad
- Choose the new action if it is less bad
- Do it all over again
The fact that computers can do this millions of times, has proven that computers can take very intelligent decisions.
Supervised learning uses labeled data (data with known answers) to train algorithms 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 predicting what kind of video you like, based on the videos you have played.
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.
Reinforcement learning is based on non-supervised learning but receives feedback from the user whether the decisions is good or bad. The feedback contributes to improving the model.
Self-supervised learning is similar to unsupervised learning because it works 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.