Bachelor’s In Machine Learning: Best Schools & Student Guide

Why Consider a Machine Learning Bachelor’s Degree

An machine learning bachelor’s degree can be a great way to kick off your career in artificial intelligence. Here are some of the main benefits machine learning degree graduates tend to see:

• Top-tier salary potential: The average salary of a machine learning engineer in the US is about $189,000, but for many, salaries can soar above $275,000.
• Specialized skills that are in demand: AI job postings now make up about 2.6% of job postings in the US. In 2025, Python (the main language that machine learning pros use) was the top skill listed in AI job postings. Data analysis and data science skills, two additional focus areas of ML students, also ranked in the top 10.
• Fast-moving space: With a degree in ML, you’ll be on the bleeding edge of technology with applications across sectors like healthcare, finance, and manufacturing. Some machine learning models are improving as much as 30% YoY.
• Multiple career paths: Earning a machine learning degree can set you up for dozens of careers. You’ll likely be qualified to work as a data scientist, a data analyst, a computer vision specialist, or a machine learning engineer. This degree is also the first step on the path to higher-level AI degrees and research-focused institutional positions.

What to Look For in a Machine Learning Degree Program

Machine learning degrees are not standardized. Some are labeled as AI, computer science, computer engineering, or even cognitive science.

For example, Western Colorado University has an Applied Machine Learning degree, and Tarleton State University offers a stand-alone BS in Artificial Intelligence and Machine Learning.

UC San Diego offers a slightly different angle in its Machine Learning and Neural Computation specialization that’s part of a BS in Cognitive Science degree.

Many more ML programs sit inside of a university’s school of computer science or computer engineering.

Although each machine learning program offers variable coursework, the ABET (Accreditation Board for Engineering and Technology) has specified that the core curriculum should be focused on technical foundational knowledge alongside programming, mathematics, and data engineering.

The best ML undergraduate degree programs combine this rigorous curriculum with additional opportunities for hands-on experiences with the latest ML tech. These beyond-the-classroom experiences show students how to translate topics like linear algebra and signal processing into real-world ML workflows.

Look for these qualifications:

• Look for schools that emphasize end-to-end, hands-on ML projects. Capstone projects, team challenges, and industry-sponsored assignments will help you walk through the entire ML workflow, from preparing data to presenting your results.
• Colleges with an R1 status are recognized as being dedicated to research and higher learning. By definition, these institutions spend at least $50 million on research annually. Take time to browse through faculty biographies to see what type of research each university’s professors are doing. Many universities allow undergraduate students to participate in research, which can offer a big boost to your resume and portfolio.
• Certain machine learning infrastructure is required for compute-intensive applications, like large computer vision projects and speech recognition models. For instance, the University of Alabama’s High Performance Computing and Data Center, set to open in 2027, will provide AI students with 14.6 MW of computing power.
• Some ML degree programs require internships or co-ops that put students directly into the real world of machine learning. Even if a university doesn’t require these types of experiences, you’ll want your school to have contacts with industry partners that can assist in your job search. For example, Kansas State offers a 14-person industry advisory board with ML professionals from Google, Gartner, Amazon, and more.

Of course, cost is a major factor when evaluating machine learning bachelor’s degree programs. There are several reputable programs with a total cost between $40,000 and $50,000, which is on par with other undergraduate degrees. However, an ML degree from a prestigious institution, like Carnegie Mellon or Stanford, can cost upwards of $200,000 for a four-year program.

Types of ML Undergraduate Degrees

There are two ways to earn an ML degree: through a dedicated machine learning degree or through a related tech major with a machine learning concentration or focus area. Within either of these two pathways, you have several options for customizing your studies.

Earning a Dedicated Machine Learning Bachelor’s Degree

A stand-alone BS in machine learning, whether offered online or in person, is a data-heavy, math-focused degree.

For instance, at BYU, machine learning majors take at least five high-level math courses and several technical data science classes.

In addition to math and data science, there are a few other focus areas that are common in dedicated ML programs:

• Computer science classes teach programming, software engineering, and algorithm analysis.
• Statistics courses cover the basics, like probability, and more advanced concepts, like Bayesian statistics and nonparametric methods.
• Core machine learning courses focus on topics like neural networks, model building, deep learning, and data mining.
• Electives can come from your area of interest. Popular options are computer vision, embodied AI, and industry-specific classes from sectors like healthcare, finance, and cybersecurity. These options are typically quite broad. For instance, students in Drexel’s machine learning program can choose from nearly 30 electives.

Getting a Bachelor’s Degree with a Machine Learning Concentration

Stand-alone bachelor’s degree programs in machine learning are still relatively rare. Another option is majoring in a related tech major while specializing in machine learning. An ML specialization can come through a formal concentration or specialty electives.

The most popular majors for an ML specialization are computer science and computer engineering. They’re a natural fit because these majors already provide students with the foundation in programming, algorithms, and mathematics that’s necessary for building machine learning applications.

Virginia Tech offers an ML program like this through a Computer Engineering degree with a Machine Learning Option. To complete the option, students take nine credit hours of machine learning classes as the focus of their electives.

Duke offers a similar program in its computer science department that’s referred to as a concentration in AI/ML. It requires 15 hours of specific coursework, including an introduction to AI, an introduction to ML, and three elective classes.

Other universities offer an ML specialization through the data science major. If you decide to go this route, you’ll have a core background in statistics, databases, visualization, and analytics. However, adding an ML specialization will require extensive math classes and at least a foundational class in programming.

Old Dominion University includes an AI/ML specialization within its data science program. It requires 30 hours of specific courses: 15 hours of advanced math and 15 hours of ML-focused electives.

Northwestern offers a combined data science and machine learning minor. To earn the minor, students must take eight courses: a Python class, a statistics class, four ML-specific classes, and two electives.

When evaluating your choice of major and university, be mindful that each program puts a different amount of focus on machine learning. Some concentrations require just a few classes, while getting a machine learning minor provides a fairly intensive study of ML.

In addition, you’ll want to browse through the academic catalog to be sure the school and major offers the kind of electives you want to take. Larger universities typically have a better variety of electives, many of them from niche areas (like computer vision, cybersecurity, robotics, and industry-specific courses for biomedical and financial applications).

If you’re interested in taking a fair amount of liberal arts electives, you may want to pursue a BA degree that offers a specialization in ML instead of the more traditional BS. This will give you a deep technical background but also allow you the time you need to add classes in business, economics, or digital humanities.

Combining a Bachelor’s Degree and Master’s Degree in Machine Learning

There are a few ML programs available that combine a bachelor’s degree with a master’s degree so that students can complete both in five or six years.

Arizona State University has a program like this. Students can graduate with a BS in data science and combine it with an MS in Data Science and Bayesian Machine Learning.

Typically, these programs are for the highest-achieving students with a GPA of at least 3.5 during their first two years of college. You can generally apply in your junior year of college and count several upper-level courses to both your bachelor’s degree and your master’s degree.

How Does a Machine Learning Degree Compare to an AI Degree?

Both ML degrees and AI degrees are growing in popularity, especially at large, research-focused institutions. But they differ slightly. An AI degree covers the broad study of intelligent systems, while an ML degree is a subfield, focused on how intelligent systems learn from data.

For example, AI programs tend to lean heavily into topics involving autonomous systems, like automated reasoning and human-AI interaction.

On the other hand, the curriculum of ML programs is concentrated in data-related topics like statistical modeling and graph neural networks.

To get an idea of the differences, here’s an example of Penn’s intro to AI class versus the intro to applied machine learning class:

Currently, most universities offer one or the other: an AI major or an ML major. A few schools name both AI and ML in one major and let students choose where to focus through their electives. Because names and academic structures vary between schools, it’s important to look at sample four-year plans to see how the classes actually differ in technical depth, elective options, and opportunities for projects.

Here are a few things that you should look for in both an AI program and an ML program:

• Python programming language
• Linear algebra and statistics
• Data collection and visualization
• Neural networks
• Ethical and responsible AI

But, for an ML degree, you’re likely to want to go deeper into these topics:

• Statistical modeling
• Regression and predictive modeling
• Data pipelines and engineering
• Model optimization and versioning

Machine Learning Bachelor’s Curriculum and Coursework

Earning a bachelor’s degree in ML is much like any other tech degree. Students start their freshman year with foundational classes and build on those classes as they work up to their senior capstone project.

Year 1

As a freshman, you’ll take a mix of general education classes and foundational ML classes. Here’s a sample of how that breaks down:

• 2-3 general education classes in communications, social sciences, and arts and humanities
• Introduction to Computer Programming
• Foundations of Engineering
• Calculus
• Discrete Mathematics

Year 2

During the second year, you’ll continue with more advanced classes. Most schools suggest a schedule with classes like this:

• Programming II
• Data Structures and Algorithms
• Probability and Statistics
• Linear Algebra
• 1-2 electives

Year 3

This is the year in which you’ll really start to get deep into ML classes or begin your ML concentration if that’s not your stand-alone major. You’ll likely finish most of your math classes prior to this year, so your course load will be full of technical classes like these:

• Introduction to AI or ML
• Database Systems
• Embedded Systems
• Signals and Systems
• Data Mining

It’s also possible to take on an internship or co-op during this year.

Year 4

Your senior year is for your most specialized ML courses and related electives. Depending on your specific interests, your class load could look something like this:

• Advanced Machine Learning
• Computer Vision
• Deep Learning
• Video Game Design
• Autonomous Systems

In your final year, you’ll complete a capstone project or an industry-sponsored project. This is a hands-on, comprehensive project meant to be a culmination of all of your studies. Here’s an example from the University of Washington:

Methodology

In order to rank the top machine learning undergraduate degrees, we scored programs based on six key factors.

• The most important factor (25%) went to hands-on practice, where we rewarded programs with embedded internships schemes, real-world applications, and capstone projects.
• Curriculum relevance comprised 20% of the score for each program. Specifically, we assessed each ML bachelor’s degree for “2026/2027-readiness”: topics like deep learning, generative AI, and natural language processing, plus the use of modern tools.
• We also gave affordability 20% weighting. We adjusted this metric to make it a value metric vs. simply rewarding the most affordable programs. In other words, programs thought to deliver world-class quality were penalized less harshly for high tuition.
• Academic quality was worth 15%. Top scores went to institutions with impressive employer reputations and world-class AI/ML research.
• 12% of our scoring came from degree focus. Standalone ML degrees scored higher than a concentration that’s only part of a general degree.
• Admissions made up the final 8%. We didn’t unduly punish highly-selective institutions, but highly accessible programs got a slight edge. Credit was also given to practical considerations, like rolling admissions and online options.

How to Choose a Bachelor’s in ML Degree

The number of schools offering ML degrees is growing rapidly. This gives you options but also makes choosing a college feel much more complicated. Some students need a flexible, affordable path directly to the job market. Others want an intensive, research-heavy program with faculty who are working on industry-leading research.

Use these profiles to help narrow down your search for an ML program.

Research-focused students: If you’d like to focus your college career on hands-on lab experiences, a stand-alone ML program at a large university might be best for you. Most of these are highly selective. You’ll likely need to have a high school GPA of at least 3.9 and a SAT score above 1500. Large universities are also most likely to offer electives and niche research opportunities. Take a look at the school’s catalog and professor profiles to see which ones line up with your research interests.

Students aiming for jobs with Big Tech firms: You’ll likely want to pursue a tech-heavy BS with an ML concentration. Look for schools with a strong foundation in math, computing, and data science, plus advanced electives like deep learning and computer vision. Those types of courses can help you build the kind of project portfolio that Big Tech employers look for. Also make sure to choose a program with industry partnerships and strong internship pipelines. Advisory boards, career fairs, employer-sponsored projects, or alumni networks can all help you land a Big Tech role.

Students aiming for graduate and PhD studies: If you know you’ll be continuing your education, look for ML programs with accelerated BS/MS options. Many of these start with a BS in computer science or computer engineering and pair it with an MS that names an ML specialization. These give you an efficient (and often cheaper) way to earn a graduate degree. Be sure to find out which courses can be double counted toward both degrees.

Students who want room to explore: You’ll want to find an ML program that offers a BA or BSA option. Another way to do it is to declare a major in a different area and earn a minor in ML. Either way, you’ll build a solid tech background while having more time to dedicate to liberal arts electives.

Budget-conscious students: If tuition is a concern, a public in-state university or online program is likely the best option for you. There are several affordable ML programs like this that provide a solid foundation in ML, data science, and computer engineering. Also look at all of your financial aid options. Some programs may surprise you. For example, Carnegie Mellon’s Pathway Program allows students with an annual family income of less than $75,000 to attend tuition-free.

Who Should Pursue a Machine Learning Bachelor’s Degree?

Machine learning is a great field for people who enjoy coding, problem solving, and working with data to find answers to real-world questions. Here are a few groups of people who are a strong fit for ML undergrad programs.

• High school graduates with a strong interest in math and computers.
• College students who are already majoring in computer science, engineering, statistics, or data science. In many cases, adding an ML minor or concentration will make your resume even more appealing to employers.
• Research-oriented students who would like a career in academia. R1 universities are the ideal place to earn a bachelor’s degree in ML and start this career. Many institutions invite students as young as freshmen to participate in research.
• Tech-field career changers who have a foundation in computer science or software development. These adults already have the basic experience in the field, but their careers could get a serious boost from having a degree in ML.

There are also people considering an ML degree who might be better suited for a different major.

• ML studies span a few different fields, but if you are particularly interested in one of those fields, it might be better to declare that as your major. For instance, if you really enjoy theory and quantitative research, majoring in a specific type of mathematics might be best. If you love deriving insights and meaning from data, a data science degree may be a better fit.
• A general computer science degree will provide the ultimate flexibility for students who aren’t quite sure where they want to end up.
• An AI-focused degree is another way to pair math and computing studies but take a wider approach to intelligent systems. It might be best for you if you want to focus on things beyond data-driven modeling like robotics or human-computer interaction.

Career Paths and Salaries for ML Degree Holders

According to the World Economic Forum’s most recent Future of Jobs report, machine learning specialists are predicted to hold one of the fastest-growing job categories through 2030. The number of jobs in this space could grow by more than 85% during that period. Here are a few of the most popular ML roles and their average salaries today.

Machine Learning Engineer

Average Salary: $278,800 (levels.fyi)

Machine learning engineers build, test, and maintain machine learning systems.

When a business faces a problem or identifies an opportunity, machine learning engineers build the data-focused technical system behind the solution. They write code, mostly in Python, to turn raw data into an organized dataset. Then they train and evaluate models to determine what works and what could perform better.

Machine learning engineers may also have a hand in the deployment of the model, like through an app or an internal tool. After deployment, they’re also responsible for testing the model regularly and ensuring there’s no model drift.

Data Scientist

Average Salary: $180,000 (levels.fyi)

Data scientists build machine learning models that focus on the research and analysis of data. They don’t typically build the ML engines behind consumer-facing apps, but they do build ML models to help businesses make better decisions.

As the name implies, the majority of a data scientist’s time is spent obtaining data, cleaning it, and exploring it. They frequently use tools and languages like SQL, Python, and R. When needed, data scientists build ML models to forecast data and outcomes. Often, they glean insights from this data and present their recommendations to leadership.

MLOps Engineer

Average Salary: $149,600 (levels.fyi)

MLOps engineers manage the lifecycle of ML models. Most often, this includes building the operational systems that support the ML model.

This role blends machine learning with software engineering, data engineering, and DevOps. Once an ML model is developed as a prototype, an MLOps engineer creates a reproducible workflow to scale the model. MLOps engineers are also responsible for building automated testing and monitoring workflows throughout production and deployment. When conditions change or the model starts to drift, MLOps engineers use a retraining workflow to test and release an updated version.

Computer Vision Engineer

Average Salary: $151,065 (Indeed)

Computer vision engineers fill a specialized role within the machine learning field. They’re specifically focused on building models that allow computers to process data from images and videos.

Like the other roles, computer vision engineers collect, clean, and organize data. However, they’re utilizing visual data alongside text. They use that data to train deep learning models and integrate them into real-world products. After deployment, they regularly work to improve the model’s speed, accuracy, and reliability. Tools like PyTorch, OpenCV, and TensorFlow are very popular in this field.