Machine Learning is relatively new field of study that draws on, intersects and overlaps with other fields such as artificial intelligence. It is a technique that relies primarily on data analytics to train computers to learn from experience as much as humans or animals do, or even better. As with many concepts, different experts define Machine Learning in different ways based on what they prioritize. Nvidia, the American Technology giant defines it as “the practice of using algorithms to parse data from it, and then make a determination or prediction about something in the world.” For Stanford University, it is the science “of getting computers to act without being explicitly programmed,” that is without relying on rules-based programming. As to the Carnegie Mellon University, the field of Machine Learning attempts to build systems autonomously improving as a result of being exposed to experience and deduce the “laws that govern all learning process.”
A more inclusive definition is that given by Daniel Faggella, the founder and CEO at Emerj as he explains that “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.” Note that definition puts the ultimate objective of Machine Learning in a nutshell, drawing on four experts that he interviewed, Dr. Yoshua Bengio from Université de Montréal, Dr. Danko Nikolic from CSC and Max-Planck Institute, Dr. Roman Yampolskiy from the University of Louisville and Dr. Emily Fox, from the University of Washington. To elaborate further, the learning process in ML relies on algorithms that enhances the performances in an adaptive way in accordance with the number of learning samples.
The objective of the machine learning algorithm is to transform data into insights and hopefully solve a problem. Imagine a retailer wants to have a forecast for next quarter’s sales, a machine learning algorithm can be quite effective here as it can utilize data related to past sales and others to predict them.
Machine Learning has ample applications in different fields, which explains the growing interests in it as well as its fast-paced development. Its algorithms’ ability to detect natural patterns and sequences and reproduce insights that can be used in predicting and decision-making is another factor that seduces experts. What increases its applicability even more is the upsurge in big data, thus rendering it essential in solving problems. In computer biology, ML is used to detect tumors, sequence DNA, discover drug. It is also used for face and object recognition and motion detection in image processing. Energy producers also use ML to forecast price and load. ML is also utilized in predictive maintenance in the automotive and aerospace manufacturing, voice recognition in natural language processing, amongst a plethora of other applications. Commenting on the prospects of Machine Learning, Bill Gates, former Microsoft chairman said “A breakthrough in machine learning would be worth ten Microsofts.”
When talking about Machine Learning, it is essential to know that there isn’t one algorithm. Simply put, an algorithm is a mathematic expression of data which is represented in the context of a problem. Each day, programmers and developers publish hundreds of new ML algorithms. These can be put into two main categories, learning style or methods (also form or function). The first simply refers to whether it is supervised learning, unsupervised learning, or semi-supervised learning, whereas the methods refers to classification, regression, decision tree, clustering, deep learning and so forth. In any case, all Machine learning consists of these three components: Representation, Evaluation, and optimization.
Representation: This refers to the hypothesis space, or the space of models (algorithms) allowed and takes into consideration that some models might be encoded much more easily than others in the formal language in which we are expressing models. This happens within one possible set and it is because the landscape of the field that a representation allows, or the possible models. For instance, the computational graph (3-layer feedforward neural networks) and support vector machines with RBF kernels are distinct types of representation.
Evaluation: the second component of Machine Learning refers to the way you assess or choose one model rather than the other. The models are evaluated using scoring function, utility function, fitness function or loss function. The CS professor at the university of Washingtin Pedro Domingos says “Think of this as the height of the landscape for each given model, with lower areas being more preferable/desirable than higher areas (without loss of generality). Mean squared error (of a model’s output vs. the data output) or likelihood (the estimated probability of a model given the observed data) are examples of different evaluation functions that will imply somewhat different heights at each point on a single landscape.”
Optimization: This refers to the way better evaluations are obtained through searching the space of represented models, which is the search process. In other words, this is the strategy of getting to the “land of models,” in Domingos’ words. combinatorial optimization, convex optimization, constrained optimization are three distinct search processes.
10 methods are in use in Machine Learning: These are regression, classification, clustering, dimensionality reduction, ensemble methods, neural nets and deep learning, transfer learning, reinforcement learning, natural language processing, and word embeddings.