Types of learning of ML
We can start saying that machine don’t learn. Machine can take an input and, passing throughtout some mathematical fomulas, can generate some outputs. This is not how animal (and human) learn things: because we can learn the same if the inputs are not pretty the same, but related to the same thing or erxplicated in a different way - something that the machine cannot do. So machine learning is the product of marketing, such as artificial intelligence (yes, that one is not intelligence for real).
Now we can take a look to different types of learning that machine can apply:
1. Supervised learning
In supervised learning:
- the dataset is the collection of labeled examples;
- each element among is called a feature vector.
2. Unupervised learning
3. Semi-supervised learning
4. Reinforcement learning
Sources
Burkov A., The Hundred-Page Machine Learning Book, 2019 Supervised Learning: Algorithms, Examples, and How It Works | Databasetown.com Unsupervised Machine Learning Algorithms and Applications | Pythongeeks.com.com A Gentle Introduction to Semi Supervised Learning | @gayatri_sharma on Medium.com Reinforcement Learning Algorithms and Applications | Techvidvan.com