Machine learning (ML), a subset of artificial intelligence (AI), refers to the ability of computer algorithms to learn patterns from data and make predictions or decisions without being explicitly programmed. In engineering, ML has emerged as a powerful tool for solving complex problems that were once considered too challenging due to the sheer scale of data and the intricacies involved in engineering systems. It involves training algorithms on vast datasets to recognize patterns, predict outcomes, optimize processes, and even automate certain tasks, transforming the way engineers approach problem-solving and design.
In the field of engineering, machine learning can be applied in numerous ways. For example, in civil engineering, ML algorithms analyze structural health data to predict potential failures in bridges and buildings, enabling preventive maintenance and improving safety. In mechanical engineering, machine learning is used to optimize the design of components and systems by analyzing performance data and suggesting design modifications that enhance efficiency and durability. Chemical engineers use ML models to optimize chemical reactions, improve yields, and reduce waste in complex processes. Similarly, in electrical and electronics engineering, machine learning aids in the design and optimization of circuits and systems, enhancing performance and energy efficiency.
It is important to understand that machine learning is a tool, not a threat to engineers. Instead of replacing engineers, ML empowers them by automating routine and repetitive tasks, allowing them to focus on creative problem-solving, innovation, and decision-making. Machine learning can handle large volumes of data and identify patterns that might not be evident to the human eye, but it lacks the domain-specific knowledge and experience that engineers bring to the table. By integrating machine learning with engineering expertise, teams can achieve better outcomes, faster development cycles, and more innovative solutions.
Machine learning has deep mathematics that govern its function. Let's consider gradient descent: the simplest mathematical approach. First, let's take a look at the expression: Θ∗=argΘmin J(Θ).
Θ∗: The optimal value of the parameter(s) Θ. argmin refers to "the argument of minimum". This would refer to a value Θ that minimizes the function J(Θ). Speaking of, J(Θ) is the "loss function". In one or two dimensions, we can think of J(Θ) as defining a surface over Θ. The same applies to higher dimensions (MIT). If we try and visualize gradient descent, we can imagine being on an arbitrary point on a surface and whichever direction the slope is negative or the "hill" goes down, that is the direction we take a small step in (MIT). Overall, gradient descent is a mathematical method for optimizing functions.
On a macro scale, machine learning models often involve millions or even billions of parameters, such as weights in deep neural networks. Gradient descent and its variants efficiently handle the optimization of these massive parameter spaces. For example, in training a convolutional neural network for image recognition, gradient descent iteratively minimizes the loss function across many layers and millions of weight parameters, ensuring the model learns features that generalize across data samples.
Numerous real-world applications of machine learning in engineering have already demonstrated its potential. In aerospace engineering, companies like Boeing and Airbus use ML algorithms to analyze flight data for predictive maintenance, reducing downtime and enhancing safety. In automotive engineering, ML is used in autonomous driving technologies to process sensor data in real-time, enabling vehicles to make split-second decisions on the road. Structural engineers utilize machine learning to predict the behavior of materials under various stress conditions, aiding in the development of stronger and more sustainable structures. In manufacturing, ML algorithms help optimize production processes by predicting equipment failures and reducing waste, leading to cost savings and improved efficiency.
Machine learning is increasingly becoming an important part of engineering, transforming traditional workflows and enabling engineers to push the boundaries of innovation. While it offers a new way to approach complex problems, it does not replace the expertise and intuition of skilled engineers. Instead, it serves as a powerful complement, enabling engineers to analyze vast datasets, predict outcomes, and optimize designs in ways previously thought impossible.
By leveraging the capabilities of machine learning, the engineering field is poised to solve some of the world's most pressing challenges in more efficient and innovative ways.
CHAPTER 3 Gradient Descent (n.d.). Retrieved November 22, 2024, from MIT.