What is Machine Learning (ML)?

General Principles of ML

The “primitive” idea behind ML is a machine which is fed by data, a “training set” and which builds a model, usually a decision, choice and strategy model, using these data in order to perform some tasks in an environment, independently of any human intervention, depending on the response of the environment, a feedback can be generated, which can take various shapes : “right” or “wrong” or a decimal score of a feature vector for instance. That feedback, coupled to the data from the environment can be integrated into the previous dataset to create a new dataset from which a ne model can be built and so on.

This presuppose the following components:

• The data (“learning set”);
• A model builder (the ML in itself);
• Task(s) to perform in an environment;
• The environment;
• Feedback from the tasks.

Learning machines and Machine Learning

At this stage, the reader understands that a learning machine is a Machine Learning with data, tasks and interaction with an environment. As itself, machine learning is doing nothing and unable to evolve if nothing is “injected” inside.

A learning machine is, therefore, a dynamical self-evolving system and the way it interacts and learns from its errors and from the environment is as much as fundamental as the algorithm/model it implements.

Machine Learning and Dynamical systems

As the ML will learn and be trained and as we iterate the training steps, the model will go through the space of all the possible models

When the learning algorithm converges, this is similar to a dynamical system reaching an equilibrium state.

The “Learning” phase of a learning machine can be seen as a time system, especially if loops are introduced. This is especially true for Recurrent Neural Networks (RNNs).

In the original concept by Turing, the learning phase of a machine is similar to the education of a child and therefore induces time, contingents, possibilities etc… which makes it relevant to be interpreted as a dynamical system.

In such an approach, concepts as Markov chains, Lyapunov stability are very relevant to ML in general.

Supervised, unsupervised and
Reinforcement Learning

The learning process of ML fall into two three different categories: supervised and unsupervised learning, or reinforcement learning.

Supervised learning can be seen as the machine been educated through a human “supervisor” which guide in some ways the learning while unsupervised learning can be seen as “wild” learning - more like raw self-organization and convergence to an equilibrium.

The difference may also be seend between the human process of learning, where learning consists mainly of education with teachers (“supervisors”) while animals learns more from their environment in general and self-adapt to that environment without supervision and this relatesd more to unsupervised learning.

What is known as “street smart” also refers largely to some unsupervised learning, eg the ability to learn without guidance and supervision into an unknown environment.

Unsupervised learning can be seen as well as Human research, facing unknown situation and learning from these unknown situations, eventually self-creating labelled categories.

A mixture of supervised and unsupervised learning is always possible. This situation is named semi-supervised learning.

In general, supervised learning in ML will use labelled data. These are data - which can be of any sort - provided with a label (either by humans either from other machines ) . This label indicates the nature of the data and how they must be interpreted by the machine. The task of ML will therefore be to consume a new unlabelled set of data and guess, predict and label them (eventually creating new labels) or classify them (fixed finite amount of labels).

A third category ( the three basic ML paradigms) is reinforcement learning. In that approach the teaching is conditioned by ‘rewards’ or ‘penalties’. The analogy with human learning is obviously the punition from parents, the fines and various punishments from the law enforcement systems or the gifts, ‘bonus’, various perks and medals provided by the educational, military or civilian bodies.

Reinforcement learning is especially relevant to game theory.

Multi-agents ML

A set of Ml units may be coordinated and cooperative. In that case we speak of Multi-agents ML. For instance a group of 1 million of ANNs which are single agents ML may be connected together - as black boxes - and cooperate together with further algorithms. This can be seen as a “team” for instance.