Machine Learning in Plain Language
By: Erik Markhauser
Tags: Machine Learning, Supervised Learning, Reinforcement Learning, Unsupervised Learning, Artificial Intelligence

Machine Learning in Plain Language

Much like living beings, machines can also learn to do things. In this case the machine is the student and the world or another being — either natural or artificial — is the teacher.

According to Tom Mitchell’s influential 1997 book, Machine Learning, there are three major parts of machine learning:

the task that needs to be learnt.

the observations for learning the task.

how well the task is going.

The tasks to be learnt include predicting knowledge or acting. How well these tasks are performed is determined by the accuracy of the predicted knowledge and the rewards and punishments resulting from actions.

According to Mitchell, machine learning occurs when a machine, as a student, gets better at the task through observing more of the world.

Learning Types

A student can predict new knowledge based on what has been observed in the world. This could include categorizing or estimating things, or understanding the possible good or bad results of actions.

Learning to Predict Knowledge

Knowledge includes a description of the current state of the world and and predicted knowledge based on the state. State can include multiple pieces of information. For example, state on an email including the sender’s email address and the text of the email. The predicted knowledge would include whether or not the email is spam based on the state.

Students can predict knowledge based on full examples given by the world or by a teacher. Full examples are those that include the antecedent and consequent knowledge. The consequent knowledge follows the antecedent knowledge. For example, the consequent knowledge of an email text (the antecedent knowledge) could be that it is classified as either spam or not spam. Using these full examples, the student finds patterns or generalized relationships, called hypotheses, that can be used to estimate future consequent knowledge with only the antecedent knowledge. This form of learning is called supervised learning.

A student can also predict knowledge without examples that include consequent knowledge. In this case the student finds patterns and commonalities in the observations that can be used to estimate consequent knowledge. This is called unsupervised learning. For example, a student can have a group of photos of objects and sort the group into categories of individual types of objects based on common characteristics — or state — in the photos. But the categories are not known beforehand. This example is a case of unsupervised learning called clustering.

Learning to Act

Students can also act based on what potential rewards or punishments can be received. The action tasks are learned through what is called is called reinforcement learning. This involves learning the desirability of states (which may be goals), and the actions that may lead to states being achieved. The student learns a policy — actions to do in certain states (i.e. situations).

For example a student can navigate a maze with the reward of reaching the end versus the punishment of running out of time. The student acts by moving from one position to another — one state to another. The desired end goal or state is to exit the maze and receive the reward.

The teacher delivers the rewards or punishments and can either be another being or the external world itself. Beings can hand out prizes; the external world can hand out punishments like rain or other natural disasters. Students learn to seek out rewards and avoid punishments, regardless of their source.

Reasoning and the Structure of Knowledge

These three forms of learning build more general knowledge from specific examples — a form of inductive reasoning. The generalizations are stored in memory and are used in new situations to either predict new knowledge or act — a form of deductive reasoning. They contain two parts of knowledge: the antecedent and the consequent. For example a policy from reinforcement learning has the antecedent as the current situation and the consequent as what to do.

Machine Learning in Practice: Algorithms

We can build machines that learn by programming them with machine learning algorithms — a series of instructions that take in inputs and produce outputs. For example, a supervised learning algorithm takes in data as the input and outputs a relationship between the antecedent and consequent knowledge. These algorithms can range from simple series of instructions to more complicated ones that even mimic the brain — which are called neural networks.

Learning as part of Artificial Intelligence

In order to function effectively, a student needs a combination of learning to predict knowledge and act. For example, supervised learning can be used to recognize situations — called states in artificial intelligence — in chess such as being in “check”. But reinforcement learning is used to figure out the most effective actions in that state to gain rewards — in this case, winning the game.

Machines have been built with artificial intelligence to handle these forms of learning, but none yet rival human ability.