An Engineer’s Role in Machine Learning

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Thanks to popular movies such as “The Terminator” and “The Matrix,” there is a good portion of the population that is suspicious of machines that can think for themselves. However, what these fictional accounts can sometimes neglect to mention – other than a strong dose of reality – is the many benefits that come from this form of advanced technology. As the quantity of data that is collected on a daily basis grows, humans are simply unable to keep up with the organization and analysis of this information. To maximize big data, machines are needed to do much of the heavy lifting.

For engineers looking to pursue a career in a cutting-edge field, machine learning is a new and exciting option that offers numerous possibilities in both research and development. Combining the skill set of an engineer with the power of computer algorithms, a career in machine learning engineering takes the best from each field to create entirely new ways to use data and technology.

What is machine learning?

In simple terms, machine learning is the facet of computer science in which professionals give computers the ability to learn a task without being specifically programmed to do so. This is accomplished through teaching computers how to find patterns in data on their own. Given data, a machine learning algorithm can recognize patterns and learn from data to make predictions about new data, all through the use of clever statistics.

In short, if you have data and a pattern in the data, your machine can learn.

As in much of engineering, however, there is obviously more to machine learning than that simple explanation when it comes to execution and delivery. Within the field, there are three types of machine learning algorithms: supervised learning, unsupervised learning and reinforcement learning. According to business analytics software and services company SAS, supervised and unsupervised are currently the most popular learning methods. They differ as follows:

Supervised learning: In this method, the algorithms are trained by entering an input and a desired outcome to create labeled examples. The machine is able to find errors by comparing the actual outcome with the outcome that it knows should be correct based on the information originally entered. An example, according to SAS, would be an algorithm for identifying credit card fraud. The machine can spot unusual charges by comparing them to the expected transactions.

Unsupervised learning: As opposed to supervised learning, unsupervised learning does not have “right” answers – or historical labels – to compare the information to. Rather, the algorithm must look at the information provided and draw its own conclusions. SAS reported that this method is helpful for finding attributes by which to sort groups, such as identifying what consumers can be targeted by the same marketing campaign.

Reinforcement learning: While not as popular as the previous two methods, reinforcement learning is an important part of the field. As opposed to supervised and unsupervised learning, this algorithm learns through trial and error, ultimately learning how to choose the option that will result in the greatest reward. According to SAS, this method is common in robotics, navigation and even gaming.

Machine learning is growing in popularity and importance in large part because companies and government agencies have large quantities of data that need to be sorted, analyzed and leveraged to ensure maximum results – and ideally a boosted return on investment. The data that is used in these algorithms can include everything from customer spreadsheets, past buyer information, murder rates, loaner information, census information, survey information, diabetes rates, website visiting rates and much more. Machine learning can not only reveal trends about this information, but can also give insight toward predicting things about future behavior, such as who is likely to pay back their loans or what customer base a specific marketing campaign should target.

While machine learning is related to the broader field of artificial intelligence, these terms are not synonyms. AI is a branch of computer science that is primarily focused on creating machines that are capable of intelligent thought. However, this is hard to accomplish without the contributions of machine learning.

“AI is basically the intelligence – how we make machines intelligent, while machine learning is the implementation of the compute methods that support it. The way I think of it is: AI is the science and machine learning is the algorithms that make the machines smarter,” Nidhi Chappell, head of machine learning at Intel, told Wired Magazine. “So the enabler for AI is machine learning.”

Applications of machine learning

Though the idea of a machine making decisions on its own and thinking independently may sound almost like a work of fiction, machine learning is actually more common than many people may expect. The general public can find elements of it in many areas of daily life. For instance, when people finish binge-watching a favorite television series, Netflix may suggest a new series that they might enjoy based on previous programs that were watched. This is an example of machine learning in a very practical application. Netflix uses an algorithm that can find common themes in a person’s previous preferences – such as a tendency toward dark comedies – and then extrapolates those preferences to find other television series or films that will likely suit that particular taste in entertainment. The same is true of similar services provided by websites, such as the way that Amazon.com recommends items that consumers might be interested in based on their browsing history and previous purchases.

While these are very useful applications of machine learning for the average person, the field is much more than shopping and entertainment. These algorithms are used in public safety, agriculture, wearable medical devices and even self-driving cars.

Computer vision is also an important application of this field. This area of study works to enable computers to act as the human vision system does, which in simple terms is to gather information from images and translate it to understanding. For example, using computer vision, a drone flying over a field could identify portions of the crop that look diseased and alert the farmer to the problem.

Machine learning and engineering

In the growing field of machine learning, engineers play an important role. Professionals with a background in electrical engineering or software engineering are usually equipped with the knowledge and skill set needed to contribute to this new field in a meaningful way.

To be successful in the field of machine learning, you should develop the following qualifications and skills:

Probability and statistics.
Applied math and algorithms.
Coding languages.
Advanced signal processing techniques.

This role is not to be confused of that of a data analyst. Though the positions are similar, the goal of a data analyst is typically to gather and evaluate information – a process completed by humans – to create usable results. Machine learning engineers typically focus more on giving computers the ability to process that information on their own. However, in the field of machine learning the responsibilities of the positions can overlap.

Career prospects in machine learning

As machine learning grows, so do the opportunities in the field for qualified professionals who want to build a career working with these algorithms. Companies as diverse as Nike, Amazon.com, IBM, Facebook and Spotify hire machine learning engineers. Though responsibilities vary, engineers in these positions usually are focused on implementing algorithms that work with company data to solve specific problems. Based on job postings on the career website Indeed, other responsibilities can include:

Developing strategies and best practices.
Building algorithms and learning systems.
Generating new code for models and improving existing code.
Building data and machine learning platforms.

Though the field is relatively new, that does not mean that compensation is low. Professionals in these positions can earn an extremely competitive salary. According to the job and salary website Glassdoor, the average salary for machine learning engineers in the U.S. is $114,826.

Beginning a career in the field

To begin a career in machine learning, start by reading. This is a rapidly advancing field, so staying up-to-date on the latest developments is critical. Monitor technology news sources and check out free books online. The more you can learn on your own in advance, the better you will be positioned to advance in a formal position once you begin working in the field.

Next, consider furthering your education. If you are interested in pursuing a career in machine learning, consider enrolling in UC Riverside’s online Master of Science in Electrical Engineering degree program to take the next step in entering this growing field through courses on topics such as power systems analysis, power system steady state and smart grids.

Engineers interested in a career in machine learning could also consider completing a Master of Science in Engineering with an emphasis in Data Science at UCR. By choosing to concentrate in data science, you will acquire the skills and knowledge you need to excel in the management of mass quantities of information in machine learning. Topics covered in the program include data mining techniques, systems engineering, statistical computing and a specific course on machine learning.