Data analysis can be performed using advanced techniques, such as machine learning (ML) and mathematical optimisation. Though ML is widespread and commonly known, mathematical optimisation is a niche area of applied mathematics that typically appeals to individuals possessing strong mathematical skills and an interest in this subject.
Possessing strong mathematical skills can significantly aid in the study of ML and mathematical optimisation. Upon comparison of the two fields, there are some notable similarities. Essential mathematical topics like linear algebra (including matrices and vectors), graph theory, multivariate calculus (such as gradient and slope calculation), and basic statistics for principal component analysis are all recommended to be proficient in, in order to excel in either field. Additionally, fluency in a programming language, such as Python, R, Octave or Julia, is advantageous.
At a glance, mathematical optimisation and machine learning may seem to have similarities. However, on closer examination, it becomes apparent that these two fields possess significant differences in terms of their characteristics and applications. Despite having some shared similarities, a more thorough comparison unveils the extent of their dissimilarities.
Examining their implementations will provide a more distinct understanding.
Mathematical optimisation: Encompasses multiple industries, including power grids, banking, global positioning systems, and factory production planning.
Autodidactic Learning Machines: Applied in various areas such as advertising, sales forecasting, product research, fraud detection, ad personalisation, and market analysis.
To gain a better understanding of the similarities and differences between the two disciplines, let us review each field more closely.
Mathematical Optimisation Theory
In the late 1940s, linear programming played a crucial role in optimisation’s establishment as a potent tool for prescriptive analysis. Optimisation is the process of selecting the best possible solution from a set of feasible candidates and has since become a popular technique for resolving complex business challenges. Mathematical optimisation is implemented to identify and execute actionable solutions quickly.
Components of Algorithmic Optimisation
Decision variables: These are the user-selected input variables represented symbolically. The value for the controlling variable is randomly selected.
Objective function: Similar to machine learning models, in optimisation, decision variables are inputted and optimised based on desired business objectives. Subsequently, numerical analysis is conducted to gain deeper insights into the matter.
Constraints (3): These requirements are based on logic and require that the objective function adheres to any physical or theoretical limitations imposed on the decision variables.
Autodidactic Learning Machines
Machine Learning (ML), a subfield of Artificial Intelligence (AI), focuses on automating tasks previously performed manually. Using data and algorithms, ML enables computers to exhibit behaviour akin to that of a human brain and continually refine their decision-making abilities. ML has become an invaluable asset for organisations seeking to enhance the efficiency and accuracy of their operations.
Arthur Samuel first introduced the concept of “machine learning” in 1959. Since then, an immense amount of data has been generated, and this technology has been extensively used in various domains. Artificial learning has become a critical component in the decision-making process, given the vast amount of data that is practically impossible for the human mind to comprehend and process.
Operation of a Machine Learning Algorithm
A machine learning algorithm can be thought of as having three primary phases:
Phase one, decision-making technique: Machine learning techniques are increasingly used in prediction and issue classification, providing a structured approach to identifying solutions based on available data. A variety of algorithms and techniques are employed to analyse data and make predictions, enabling more precise and efficient decision-making processes.
Phase two, error function: To evaluate the effectiveness of two different models’ predictions, it is useful to have a standard for comparison. Using a mathematical formula, an objective quantitative measure can be obtained to compare and assess the accuracy of these models.
Phase three, refinement or improvement process: By iteratively modifying its weights and biases, the algorithm can determine the optimal way to include the data. The process is repeated until the desired level of accuracy is attained.
Common Machine Learning Challenges
Given its adaptability to new data and changes, machine learning is employed in an extensive range of applications. These potential use cases may be broadly categorised into three groups based on anticipated results.
Category 1, regression: Predicting a continuous dependent variable based on the relationships between multiple independent factors is a common problem addressed with supervised algorithms. Supervised algorithms are well-suited for this type of task as they can detect patterns in data and use them to predict future outcomes. By using these algorithms, it is possible to accurately predict a continuous dependent variable from a set of independent factors.
Category 2, classification: This technology utilises algorithms that have been trained to identify patterns within datasets and assign observations to categories based on the shared characteristics across multiple independent variables.
Category 3, clustering: Clustering is an unsupervised learning technique that differs from categorisation in that the user is not required to specify the number of groups to which the data points should be assigned. Instead, an algorithm is used to determine the number of clusters and their distribution. This technique heavily relies on unsupervised learning algorithms to group the data points accurately into meaningful clusters.
Comparison and Contrast
Employing advanced mathematics as a basis for advanced technologies such as mathematical optimisation and machine learning demands significant information and processing capabilities. Adopting both of these technologies can provide companies with substantial benefits in terms of their ability to make informed decisions, and these benefits are expected to grow as data production continues to improve.
At first glance, the similarities between these two technologies are more significant than their differences.
There are four distinct types of analytics that have been identified. Machine learning provides an invaluable predictive analytics tool, capable of processing vast volumes of historical data and aiding in the creation of strategies that could potentially increase a company’s profitability. Conversely, mathematical optimisation can be leveraged to provide guidance prior to conducting an analysis. It utilises the most current data to develop solutions based on appropriate mathematical models and algorithms. This allows for swift and dependable decisions to be made in everyday situations.
It is clear that both machine learning and mathematical optimisation technologies are employed in a wide range of contexts. If adequate data is provided, machine learning can be applied to an almost limitless range of applications. People worldwide are using technologies that rely on machine learning to become increasingly efficient and accurate over time. Speech recognition, virtual assistants, recommendation systems, and spam filters are among the most notable examples. Moreover, decisions regarding personnel management, goods delivery, and energy distribution can benefit from the use of mathematical optimisation.
With ongoing research and development, both of these technologies have become versatile resources that are in high demand among businesses and organisations. These entities are willing to invest significant resources to acquire state-of-the-art solutions that assist them in making effective decisions.
