2016
Introduction to Machine Learning Applied Research Partners
Machine Learning is an integral part of information technology, rarely understood and almost always referenced with ‘robots’ and ‘artificial intelligence’.
Machine Learning | 1 1 An Introduction to Machine Learning @IsaacKurira Applied Research Partners
Machine Learning
is
an
integral
part
of
information technology, rarely understood and almost always referenced with ‘robots’ and ‘artificial intelligence’. The truth is, machine learning is everywhere in our lives from credit card companies assessing credit default risks of potential customers, insurance companies assessing the likelihood of insurance pay-outs, health researchers predicting risks of diseases within populations or just traffic lights quietly regulating traffic flow. The purpose of this short text is to describe to the reader what is at the heart of machine learning and lay the foundations for a basic understanding.
As psychologists, we study the process of learning in animals and humans through for example developmental psychology or cognition where we present individuals with certain stimuli (Input) and observe the reaction (Output). The process of machine learning in all respects is the same; we provide information (Input) to our machine and observe the reaction (Output). Beyond all technical knowledge, at its core, the art of machine learning relies on this very basic process of input/output and the application of statistics to make inferences, predictions and decisions. So, what do you input and what does it output? When a speech recognition application is able to improve its performance because it has heard numerous samples of your voice, you can clearly see the workings between Input (user voice samples) and Output (performance of tasks related to the spoken commands by the user). The ‘special sauce’ that makes machine learning so interesting and what captures our imagination is not the ability of the machine to behave according to what you have already taught it in terms of explicit inputs, it is the implicit ability of the machine to construct relationships and patterns between input/output. This is the beautiful art of machine learning; the ability to ‘learn’ without being explicitly taught how to do so.
Machine Learning | 2 But
how can the machine learn? Your machine can learn in two ways; either through
supervised learning in which the user has a clear output expected and assesses the correctness of the prediction to further understand the relation between the given input/output states. A common example of supervised learning is Regression Analysis. Alternatively through unsupervised learning, the user has no explicitly expected outcome but rather takes a lead from the information gleamed by the output as to how the input information is related. Such unsupervised learning commonly gives ‘features’ of the input data and so a common method of unsupervised learning is a process called feature selection/feature extraction. As humans we learn to speak through inputs of language. As machines, they learn through code. The sequence of instructions written in code to provide the output is what we refer to as the algorithm. As a human you would not say “As a child, I learnt how to speak through an algorithm my parents gave me”, we would however say “I coded an application that can speak through an algorithm I gave it”. Supervised and unsupervised machine learning is conducted through the input of algorithms coded by the user. To further complicate things, you can code algorithms which find the best algorithms to perform any given task.