•	Inductive reasoning 
•	Connectionism
•	Evolutionary Computation 
•	Bayes’ theorem 
•	Analogical modeling 

Key takeaway: Pedro Domingos believes the “Master algorithm” will be a mixture of different schools of machine learning, as each school of thought has strengths and weaknesses that challenge their ability to be applied universally. 


University of Michigan - Ann Arbor

There are several categories for ML algorithms, including:
 - Supervised Learning
 - Semi-supervised Learning
 - Unsupervised Learning
 - Reinforcement Learning
 - Feature Learning (Representation Learning)

The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.


Machine Learning Categories

https://www.amazon.com/Master-Algorithm-Ultimate-Learning-Machine-ebook/dp/B012271YB2

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Unsupervised machine learning methods find structure in unlabeled data.
They are used when we DON'T have prior knowledge about the categories.

Unsupervised statistical learning

In reinforcement learning, the machine goes through trial and error processes where it is rewarded/penalized for the actions it performs. The machine's goal is to maximize the total reward by leveraging previous attempts to make the next decision. The model starts from completely random trials and eventually leads to something with very sophisticated tactics and skills. Compared to supervised/unsupervised learning, reinforcement learning is much more advanced. A reinforcement model will continuously learn, unlike supervised/unsupervised models which all have an endpoint after the training and test data phases.

Reinforcement Learning

- Feature learning, also called representation learning use machine learning to discover not only the mapping from representation to output but also the representation itself. A feature learning algorithm can discover a good set of features for a simple task in minutes, or for a complex task in hours to months. Feature learning can be either supervised or unsupervised.

- Representation of information to be processed has a great importance in day to day life since it eases the task of information processing. For example usage of Arabic numerals instead of Roman numerals for mathematical operations.

- In the context of machine learning an acceptable representation of data makes the future learning tasks easy. The successive learning tasks decide the choice of information representation.



Feature Learning (Representation Learning)

Machine learning schools of thought (as explained in ”The Master Algorithm” by Pedro Domingos):

What are the categories of machine learning algorithms?

Supervised learning is a type of machine learning problem where the objective is to predict a designated unknown label based on known inputs. This approach requires a training dataset consisting of examples for which the ground truth labels are already known, allowing the model to learn the mapping from inputs to classifications.

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