Machine learning is widely considered to be one of today’s hottest fields. But many of today’s students are unaware of what machine learning is and why it matters so much. Wondering whether you’ve got a future in machine learning? Here’s a closer look at this increasingly important area, along with why it matters so much.
What is Machine Learning?
SAS defines machine learning as “a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.”
Princeton University lecturer Rob Schapire puts it in simpler terms: “Machine learning studies computer algorithms for learning to do stuff. We might, for instance, be interested in learning to complete a task, or to make accurate predictions, or to behave intelligently. The learning that is being done is always based on some sort of observations or data, such as examples, direct experience, or instruction. So in general, machine learning is about learning to do better in the future based on what was experienced in the past.”
Why Machine Learning Matters
With the power of machine learning, says SAS, “it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.” This leads to improved decision-making capabilities independent of human intervention with applications in a broad range of industries, including financial services, government, healthcare, marketing and sales, oil and gas, and transportation.
Machine learning is so promising, in fact, that Business Insider recently declared it to be “a revolution as big as the internet or personal computers.” With a track record of world-changing developments including everything from Amazon product recommendations to Google’s self-driving car, machine learning has already changed the world and how we live in it.
But that’s all just the beginning, according to experts like computer scientist and author of “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake our World” Pedro Domingos, who told BI, "There were two stages to the information age. One stage is where we had to program computers, and the second stage, which is now beginning, is where computers can program themselves by looking at data."
Meanwhile, Google’s executive chairman Eric Schmidt forecasts that machine learning “will be the basis and fundamentals of every successful huge IPO win in five years.”
Machine learning is also lauded for its potential to improve customer care by automating certain tasks. Machines don’t always outperform humans -- especially in matters of high-touch decision making -- but in improving both efficiency and efficacy where technology prevails, machine learning can free people up to focus on what they do best.
And while we often think of machine learning as future terrain, it’s also happening all around us, including in the higher education space as a means of improving teaching and learning. Moving forward, it will support unprecedented personalized learning for use by everyone from students to advisors. In other words, with a background in machine learning, you can not only change the world, you can also apply what you know much closer to home.
Is Machine Learning for You?
Of course, machine learning studies aren’t for everyone. But if you possess an interest in and aptitude for computer science fundamentals and programming; probability and statistics; data modeling and evaluation; and software engineering and system design, you may be suited for an in-demand career in this red-hot field.
The reality is, however, that if you want to “future-proof” your career, these subjects may be the key.
Concludes The Atlantic on career planning for today’s college students, “Students who are embarking upon their college studies should embrace one of two possible career strategies. The first is to look for jobs that are likely to favor human capabilities over artificial intelligence—jobs that depend less on having great swathes of technical knowledge than on having creativity and strong interpersonal skills, such as the ability to empathize. The second career strategy is to aim to be directly involved in the development and delivery of these increasingly capable systems, for example as a systems engineer, a data scientist, an AI specialist, or a knowledge engineer. In short, students can plan to compete with machines or to build the machines.”
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