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

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

This learning approach trains an agent by letting it interact with an environment and observe the consequences of its actions. After each action, the agent receives feedback in the form of rewards or penalties, then adjusts its behavior to improve future outcomes. The objective is to choose actions that maximize long-term reward, not just the next immediate payoff. This is especially useful for tasks where decisions affect later results, such as game playing, robot control, or scheduling.

Learning from Rewards and Penalties

- 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)

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

•	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. 


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

Supervised learning is a method for learning a mapping from inputs x to outputs y from labeled examples (x, y). Common supervised learning algorithms include linear regression, logistic regression, and neural networks.

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