Master’s in Machine Learning: A Complete Guide to Machine Learning Master’s Programs Online & On-Campus

Written by Scott Wilson

automation of data analytics

Within the burning flames of artificial intelligence research, there’s no technique or technology hotter than machine learning. Look at any of the major flare-ups in AI in the past few years, and you’ll find ML (Machine Learning) applying the gas:

These are miracles of software reasoning and just the latest examples of useful applications of machine learning. While the overall field of AI languished in the back seats of computer science classrooms and scrambled for table scraps for research funding, ML was already powering up industries like data science, cybersecurity, and object recognition.

None of these advances have come easy, however. In every case, they’ve been the result of hard work and experimentation by coders and researchers who have spent years building the core skills behind ML… mastering subjects like math, programming, logic, statistics.

To master such fields in the right blend for a successful ML career, a master’s degree in machine learning is the natural choice.

What Is Machine Learning?

robot machine learning graduateMachine learning has been used for a lot more than just AI applications. It’s a computational technique that has uses outside of the pursuit of artificial intelligence, particularly in fields where statistical analysis is useful or large amounts of data need to be processed.

Amazon’s recommendation system uses ML to discern trends in your purchasing history and interests. Email hosting providers use it to filter spam out of your feed before it ever hits the inbox. HVAC systems implement it to reduce power consumption to achieve better cooling with less energy. There is a world of opportunities for highly trained machine learning experts.

Machine learning powers many of the systems that we have come to take for granted in the technology we use every day.

At heart, the field uses applied knowledge of statistics to create algorithms that can recursively process data to generalize accurate predictions or distillations of knowledge. The use of multiple layers in this process, each working on different discrete aspects of the data, creates neural networks capable of performing processes resembling human reasoning.

The depth and complexity of these networks has been spurred by advances in both algorithmic modeling and computational ability. Machine learning experts today are still pushing forward the state-of-the-art in ML modeling as well as putting it to new uses across a range of industries.

What Can You Do with a Master’s in Machine Learning?

video lessons from homeThis means that earning a master’s degree in machine learning sets you up with a diversity of opportunities that isn’t always the case with purely AI-oriented degrees.

Building machine learning skills is a bit like learning woodworking skills. You can take a talent for carpentry and use it to create fine furniture pieces, install cabinetry, or build boats. By developing the most fundamental math, programming, and statistical skills for ML, you can work on higher functions in just about any field.

Of course, artificial intelligence is the name of the game today. Within that expanding field, careers today tend develop through one of three funnels:

Machine learning experts can find work in any of those AI-related areas, or in the dozens of other specializations that ML is widely used in today.

So your ML career options are diverse… and lucrative. But you can improve your odds of getting into the field you want by picking the right kind of machine learning master’s degree.

Choosing the Tools in Your ML Toolbox Through Various Types of ML Master’s Degree Programs

using a tablet to control robotThere are many specializations in machine learning. You will find a range of degree titles and different course plans to reflect them. All require around 30 credits of coursework and take anywhere from one to three years to complete.

The most common are the basic Master of Science in Machine Learning, or the closely related Master of Professional Studies in Machine Learning. Both programs offer the same core concepts and often identical coursework; the MPS will have a greater focus on practical applications of machine learning while the MS is more research oriented, and a better fit for compsci AI roles.

There are also many master’s degrees that deliver ML training with even more specific applications in mind. The Master of Science in Artificial Intelligence and Machine Learning is one path; a Master of Science in Data Science and Machine Learning is another.

Finally, you can also explore ML education as a concentration offered on other types of measures. The Master of Science in Artificial Intelligence Machine Learning concentration flips the script on an MS in ML AI focus but covers the same ground. A Master of Computer Science Machine Learning Concentration adds an ML focus to general compsci studies.

A Master of Engineering in Data Science and Machine Learning or a Master of Electrical Engineering Machine Learning and Signal Processing focus aim for specific engineering applications of ML. And a Master of Engineering Management Data Analytics and Machine Learning track comes with leadership and organizational training to nudge you more toward business applications.

Then there are the highly specialized degree programs in sub-fields of AI that have a strong ML core: degrees like the Master of Science in Natural Language Processing or Master of Science in Computer Vision necessarily revolve around ML studies today, even though their field of study is limited in application. They are a perfect fit for the highly technical, specialized roles ML and AI are finding in fields like medicine and transportation today.

Getting Into Master’s in Machine Learning Programs Requires Significant Preparation in Math and Programming

quantum mechanics

Like AI degrees, you need to bring strong math and programming skills with you when you apply for ML master’s programs. Machine learning is a technique that makes use of powerful, advanced math and statistics. It requires a command of basic computational and programming concepts that can take years to master themselves. There’s no time to get you up to speed on the basics.

While some colleges offer special preparatory coursework for some students separate from the master’s program itself, your best bet is to prepare by earning a relevant bachelor’s degree first.

Traditionally, the most popular programs for this purpose are those in the most relevant majors:

But as more and more universities come up with bachelor’s programs in artificial intelligence or machine learning themselves, you can also look in that direction to lay the groundwork for a successful application to a top-line ML master’s program.

Looking at the Curriculum Offered in Master’s in Machine Learning Degrees Today

Regardless of the direction or specialization, machine learning master’s programs today come with a common grounding the advanced fundamentals of the subject. That means required core classes such as:

Machine Learning Fundamentals and Applications

This covers the essential groundwork of how ML breaks down problems in reasoning and applies statistical methods to infer or generate results. It also will go over ML applications in a range of fields more broadly to help you understand how the technique is used.

Principles of Data Science and Structures

ML today revolves around crunching large volumes of information. These classes familiarize you with data management, storage, cleaning, and processing.

Statistical Techniques and Optimization in Machine Learning

Ultimately, ML has proven as powerful as it is today because of its ability to be trained in an automated way toward better and more general performance. Learning the statistical and mathematical background and techniques such as convex optimization for developing such algorithms is a core part of ML education.

Deep Learning Algorithms and Digital Neural Networks

Most ML master’s degrees today will also have extended coursework on the area that has seen the biggest breakthroughs in recent years: deep learning, the layered technique responsible for building the most effective neural networks. These classes explore popular frameworks and coding techniques for developing deep learning systems.

Naturally, your core coursework will also vary according to any specialization or focus area. AI-centric programs can expect more courses in generative processes, logic and reasoning, and AI ethics. Those focused on data science will have expanded classes in data mining, big data storage and management, and information retrieval.

Machine Learning Master’s Programs Offer Electives to Specialize in Your Own Interests

robot engineer programmerOn top of your core courses, you’ll get around ten credits to sink into elective class options of your choosing.

In general, you can split elective options at most schools into two categories. The first deals with professional applications of ML, which comes through classes like:

The second takes you deeper into theory and specific ML techniques, in courses with titles such as:

These can either broaden or deepen your education in ML or related areas. You can use the options to branch out into well-known specializations or to explore areas where ML is still in its infancy. They can give you a unique combination of expertise spanning multiple related fields, or offer a deep dive into a single aspect of ML.

A Final Project Seals the Deal in Your Master’s in AI and Machine Learning Degree

Demonstrating your mastery of the field is the ultimate objective of a master’s program. That means completing a final project of some kind. Some degrees have only a single track, but many master’s programs in machine learning offer you two options:

Your topic of investigation will set the tone for your career, so your final project is really an opportunity to get a head start on the kind of ML you want to practice.

In both cases, the project is designed to both help you synthesize all the various material you have studied and to demonstrate your capabilities as a machine learning expert. It’s going to be the first thing potential employers look at after graduation. Universities also insist that master’s projects produce rigorous, high-quality results to maintain their own standards and reputation.

Picking the Perfect School for Your Master’s in Machine Learning

happy students outside between classesReputation is also one of many factors that you will be considering as you decide on the right university for your machine learning master’s program.

As a legacy aspect of AI, ML has been around long enough for clear leaders to emerge in the educational world. Reputation is important when considering a good master’s program. So it’s worth looking at assessments from outside organizations, doing a review of published research coming out of the department offering the program, and seeing what recent graduates have to say.

The quality of professors will have a lot to do with driving the rigor of ML studies and setting the bar for achievement. Highly respected professionals with both academic and real-world experience are key. Their research programs will dictate the areas of expertise you will find at a given school. Their interest in supporting your success will determine the kind of classroom experience you have.

Research centers and academic support systems are also a big deal at this level of study. You want to see programs that support your areas of interest and offer opportunities to investigate questions at the cutting edge of modern machine learning. And counseling and mentorship access can be key for absorbing the big ideas in this field.

You want a university that offers a master’s in AI and machine learning that tracks with your professional goals.

Pursuing a Master’s in Machine Learning Online Is Standard Today

distance learning themeWith coursework that revolves around code and ideas, and study that is often intensively individual, machine learning is a good fit for online or hybrid degrees… and many schools offer them.

The very fact that these have become common is a boon to you in your search for a program that delivers the perfect fit. By expanding the range of schools you can realistically attend without relocating, you have more options to choose from.

Remote master’s in machine learning programs unlock options at schools around the country.

And with the asynchronous classes that commonly are used in online programs, you can keep your current career ticking without interruption. Completing your coding coursework after dinner, streaming a lecture on your lunch break, or participating in a group chat about a class project during your commute frees you up from the constraints of traditional classes. Your family will appreciate it, and so will your employer.

Online degrees also come with some cost savings typically baked in. Without having to drop your job, you can keep the income you already rely on. And since you don’t have to pick up and move, you can take advantage of cost savings that come from staying at home while you study.

What Are the Costs of Earning a Master’s Degree in Machine Learning?

reading in the libararyYou’ll appreciate the potential for cost savings in online machine learning programs.

According to NCES, the National Center for Education Statistics, the average annual cost for graduate degrees in 2022 came to $20,513 for tuition and fees. That counts out room and board and any costs for course materials.

Private schools, of course, cost more… $28,017 per year for some of the top ML programs in the country. But if you can find a good degree offered at a public school in your state, you can save a lot. NCES shows graduate studies for in-state residents average only $12,596 per year.

Either way, you’re looking at somewhere between $25,000 and $60,000 just in tuition for a master’s in machine learning.

It’s a growing field where you will find that a big investment in your education pays long-term dividends, however. With the flexibility that comes from advanced studies in machine learning, you can tap into the exciting prospects of artificial intelligence as they grow. And at the same time, you have a built-in fallback in any of the dozens of other areas where ML already has a solid standing.