Data Tools Deep Dive: Machine Learning

Contributing Author: Dr. Michael Tamir

Whether you knew it or not, you’ve probably been taking advantage of the benefits of machine learning for years. Most would find it hard to go a full day without using at least one app or web service driven by machine learning. Amazon, Facebook, Netflix, and, of course, Google all have been using machine learning algorithms to drive searches, recommendations, targeted advertising, and more for well over a decade. In recent years, letting algorithms sort through a company’s data to find out how to optimize a business has become the rule rather than the exception. This extends not only to digital business models like web services or apps, but also to any company or industry where data can be gathered: energy, financial services, marketing, transportation, brick-and-mortar retail, and even how you get you coffee from your local coffee shop.

Though the term “machine learning” has become increasingly common, many still don’t know exactly what it means and how it is applied. We will examine how machine learning is defined as a tool used by data scientists — and take a bird’s eye view of how it was developed, how it is currently being used, and what lies ahead as it continues to evolve.  

Defining Machine Learning

The basic concept of machine learning involves the use of statistical learning and optimization methods that let computers analyze data sets and identify patterns based. The typical machine learning algorithm consists of (roughly) three components:

  1. A decision process: A recipe of calculations or other steps that takes in the data and returns a “guess” at the kind of pattern in the data your algorithm is looking to find.
  2. An error function: A method of measuring how good the “guess” was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
  3. An updating or “optimization” process: This is where the algorithm looks at the miss and then updates how the decision process comes to the final decision so that the next time the miss won’t be as great.

For example, if you’re building a movie recommender, your algorithm’s decision process might look at how similar a given movie is to other movies you’ve watched and come up with a weighting system for different features. During the “training process” it goes through the movies you have watched and weights different properties like if it is a sci-fi movie or if it is funny. The algorithm then tests out if it ends up recommending movies that you (or people like you) actually watched. If it gets it right, the weights it used stay the same, if it gets a movie wrong, the weights that led to the wrong decision (e.g., action-adventure) get turned down so it doesn’t make that kind of mistake again.

Since a machine learning algorithm updates autonomously, the analytical accuracy improves with each run as it teaches itself from the data it analyzes. This iterative nature of learning is both unique and valuable because it occurs without human intervention — providing the ability to uncover hidden insights without being specifically programmed to do so.

The Evolution of Machine Learning

Although advances in computing technologies have made machine learning more popular than ever, it’s not a new concept. The origins of machine learning date back to 1950. Alan Turing, speculating on how one could tell if he or she had developed a truly integrated “artificial intelligence” (AI), created what is now referred to as the Turing Test, which (roughly) suggests that one way of testing for whether or not the AI is capable of understanding language is to see if it is able to fool a human into thinking he or she is speaking to another person. In 1952, Arthur Samuel wrote the first learning program for IBM — this time involving a game of checkers. The work of many other machine learning pioneers followed, including Frank Rosenblatt’s design of the first neural network in 1957 and Gerald Dejong’s introduction of Explanation Based Learning (EBL) in 1981.

In the 1990s, a major shift occurred in machine learning when the focus moved away from a knowledge-driven approach to that of data. This was a critical decade in the field’s evolution, as scientists began creating computer programs that could both analyze large data sets — and learn in the process. Several milestones in machine learning are marked by instances where an algorithm is able to beat the performance of a human being, including Kasparov’s defeat in chess at the hands of IBM’s Deep Blue in 1997, IBM Watson’s 2011 victory in Jeopardy, and more recently the victory of the Alpha Go algorithm over Sedol playing Go, a game notorious for its massively large space of possibilities in game play.

Near-Future Applications of Machine Learning

Today, machine learning is being used around the world in nearly every major sector— including business, government, finance, agriculture, transportation, cybersecurity, and marketing. Such rapid adoption across disparate industries is evidence of the value that machine learning creates. Armed with insights from vast data sets — which often occur in real time — organizations can operate more efficiently and gain a competitive edge.

The applications of machine learning extend beyond commerce and optimizing operations. Following its Jeopardy win, IBM applied the Watson algorithm to medical research literature “sending Watson to medical school.” More recently, precision medicine initiatives are breaking new ground using machine learning algorithms driven by massive artificial neural networks (aka “deep learning” algorithms) to detect subtle patterns in genetic structure and how one might respond to different medical treatments. Over the last few years, breakthroughs in how machine learning algorithms can be used to represent natural language have enabled a surge in new possibilities that include automated text translation, text summarization techniques, and sophisticated question and answering systems. Other breakthroughs involve learning systems for automated robotics, self-flying drones, and the promise of industrialized self-driving cars.

The continued digitization of most every sector of society and industry means that an ever-growing volume of data will continue to be generated. The ability to gain insights from these vast data sets is one key to addressing an enormous array of issues — from identifying and treating diseases more effectively, to fighting cyber criminals, to helping organizations operate more effectively to boost the bottom line.

The universal capabilities that machine learning enables across so many sectors makes it an essential tool — and experts predict a bright future for its use. In fact, in Gartner’s “Top 10 Technology Trends for 2017,” machine learning and artificial intelligence topped the list:

AI and machine learning… can also encompass more advanced systems that understand, learn, predict, adapt and potentially operate autonomously… The combination of extensive parallel processing power, advanced algorithms and massive data sets to feed the algorithms has unleashed this new era.

As machine learning and AI applications are becoming more popular, they’re becoming more accessible too — moving from server-based systems to the Cloud. Over the past few years, Google, Amazon, Microsoft, Baidu and IBM all unveiled machine learning platforms through open source projects and enterprise cloud services.

Machine Learning and datascience@berkeley

In recognition of its critical role both today and in the future, datascience@berkeley has included an in-depth focus on machine learning in its Master of Information and Data Science (MIDS) curriculum.

The foundation course is Applied Machine Learning, which aims to provide a broad introduction to the key ideas in machine learning. The emphasis is on intuition and practical examples rather than theoretical results, though some experience with probability, statistics, and linear algebra is important. Students learn how to apply powerful machine learning techniques to new problems, run evaluations and interpret results, and think about scaling up from thousands of data points to billions.

The advanced course, Machine Learning at Scale, builds on and goes beyond the collect-and-analyze phase of big data by focusing on how machine learning algorithms can be rewritten and extended to scale to work on petabytes of data, both structured and unstructured, to generate sophisticated models used for real-time predictions. Students learn how to skills using cutting edge distributed computation and machine learning systems such as Spark and are trained to code up their own implementations of large-scale projects, like Google’s original PageRank algorithm, and how to use modern deep learning techniques to train text-understanding algorithms in the Natural Language Processing course. 

Learn more about the datascience@berkeley curriculum.