Machine learning (ML) and mathematical optimisation are state-of-the-art techniques for deriving knowledge from data. While the term “machine learning” is probably familiar to most people due to its wide usage and popularity, mathematical optimisation is a more specialised field of applied mathematics that is more likely to be pursued by those with a strong mathematical background and an interest in the topic.
Mathematical skills are highly beneficial when studying both fields of study. Upon comparison, there are some similarities that can be noted. Linear algebra (such as vectors and matrices), graph theory, multivariate calculus (for example, slope and gradient calculation) and some basic statistics for principal component analysis are all recommended to be well-versed with in order to excel in both. Furthermore, fluency in a programming language, such as Python, R, Octave or Julia, is a definite plus.
At first glance, the fields of mathematical optimisation and machine learning may appear to be similar. On closer examination, however, it becomes evident that these two areas possess numerous differences in terms of their features and applications. Although they share some similarities, further comparison reveals the extent to which they differ.
Looking at their implementations will give you a clearer picture.
For Mathematical optimisation: Everything from power grids and banking to global positioning systems and factory production plans.
Autodidactic Learning Machines: Applications in advertising, sales forecasting, product research, fraud detection, ad personalisation, and market analysis.
Let’s examine the disciplines themselves to have a better grasp on the parallels and divergences.
Optimisation Theory in Mathematics
In the late 1940s, linear programming played an instrumental role in the emergence of optimisation as a powerful tool for prescriptive analysis. Optimisation is the process of identifying the best possible solution from a set of feasible candidates and has since become a popular decision making technique for tackling complex business challenges. Through the use of mathematical optimisation, actionable solutions can be identified and implemented quickly.
Algorithmic Optimisation: Its Constituent Parts
Decisional factors: They are the user-selected, symbolically-represented input variables. The value for the regulating variable is picked at random.
Objective purpose: What we are looking at is an analogy to a model used in machine learning, whereby the decision variables are inputted and optimised based on the desired business objectives. Following this, a numerical analysis is then conducted in order to gain further insight into the matter.
Limitations (3): These requirements are logical in nature, and necessitate that the objective function be in compliance with any physical or theoretical restrictions that may have been imposed on the choice variables.
Automatic Learning Machines
Machine Learning (ML), a subset of Artificial Intelligence (AI), is focused on automating processes that were done manually in the past. It enables computers to demonstrate behaviour similar to that of a human brain by using data and algorithms to continually advance their decision-making abilities. ML has become an invaluable tool for organisations to improve the efficiency and accuracy of their operations.
The concept of “machine learning” was first introduced by Arthur Samuel in 1959. Since then, there has been a significant increase in the amount of data being created, and this technology has been used to great effect in various contexts. Artificial learning has become a vital tool in the decision-making process, since it is practically impossible for the human mind to comprehend and process such a vast amount of data.
Functioning of a Machine Learning Algorithm
You may think of a machine learning algorithm as having three main phases:
One, a method of deciding: Machine learning techniques are becoming increasingly popular in the areas of prediction and issue classification, as they provide a systematic approach to identifying solutions based on given data. Various methods and algorithms are employed to analyse the data and make predictions, allowing for more accurate and efficient decision-making processes.
Second, a function of errors: When evaluating the efficacy of two different models’ forecasts, it is beneficial to have a benchmark for comparison. A mathematical formula can provide an objective, quantitative measure for contrasting and assessing the accuracy of these models.
3. a procedure for upgrading or bettering: By continually adjusting its weights and biases, the algorithm is able to identify the most effective method of incorporating the data. This process is repeated until the desired accuracy is achieved.
Typical ML Issues
Due to its ability to adjust to changes and new data, machine learning is being used for an ever-increasing variety of applications. Based on expected outcomes, these possible use cases can be broadly divided into three categories.
1. falling back: Predicting a continuous dependent variable based on the relationships between multiple independent factors is a common problem for which supervised algorithms are employed. Supervised algorithms are well-suited for this type of task as they are able to identify patterns in data and use those patterns to make predictions about future outcomes. By using these algorithms, it is possible to accurately predict a continuous dependent variable from a set of independent factors.
Second, categorise as: This technology employs algorithms that have been specifically trained to identify patterns within data sets and categorise observations according to the commonalities shared between them across multiple independent variables.
Clustering, Third: Clustering is a form of unsupervised learning that differs from categorization in that the user does not have to specify the number of groups into which the data points should be grouped. Instead, an algorithm is used to determine the number of clusters and how the data points should be distributed among them. This technique relies heavily on unsupervised learning algorithms to accurately group the data points into meaningful clusters.
Comparing and contrasting
The use of advanced mathematics as a foundation for cutting-edge technologies such as mathematical optimisation and machine learning requires significant information and processing power. Investing in both of these technologies can provide companies with substantial benefits in terms of the decision-making options they offer, and this beneficial impact is likely to increase further as data production continues to improve.
When compared on the surface, these similarities outweigh the differences between the two technologies.
There are four distinct types of analytics that have been identified. Machine learning offers an invaluable predictive analytic tool that can process vast amounts of past data and assist in the formulation of strategies that could potentially increase a company’s profitability. Mathematical optimisation, on the other hand, can be utilised to provide advice prior to carrying out an analysis. This approach makes use of the most up-to-date data to develop solutions based on the most suitable mathematical models and algorithms, thereby allowing for rapid and reliable decisions to be made in daily life.
It is evident that both machine learning and mathematical optimisation technologies are employed in a vast array of contexts. If the necessary data is provided, machine learning can be applied to an almost endless array of use cases. The technologies that are utilised by people around the world make the most of machine learning to become more efficient and accurate over time. In this regard, speech recognition, virtual assistants, suggestion systems, and spam philtres are some of the most exemplary examples. Furthermore, decisions such as organising personnel, delivering goods, and distributing energy can be improved with the aid of mathematical optimisation.
With the continuous advancement of research and development being done on a daily basis, both of these technologies have grown to become multifunctional resources that are highly sought-after by businesses and organisations. These entities are willing to invest considerable funds in order to obtain cutting-edge solutions that aid them in making effective decisions.