AI vs. machine learning: What’s the difference, and what will you learn in an AI/ML degree?

Artificial intelligence is a term we throw around casually these days, as if we all agree on what it means. People use it to talk about ChatGPT, recommendation engines, fraud alerts, self-driving cars, and almost any technology that seems to make decisions.

But what exactly is artificial about it? What counts as intelligence? And when we put those words together, are we describing actual intelligence, or something that simply behaves in ways we associate with intelligence?

The word artificial can mean different things depending on the context. Sometimes it means fake or imitation. But in the term artificial intelligence, it means something created by people rather than something that occurs naturally. The distinction is between natural intelligence, such as human intelligence, and intelligence-like capabilities created through computers.

Intelligence, meanwhile, generally refers to abilities such as learning, reasoning, recognizing patterns, solving problems, and adapting to new information.

Put the two ideas together, and artificial intelligence refers to human-made computer systems designed to perform tasks that, if a person were doing them, would involve some form of intelligence. An AI system might recognize an object in a photo, predict what someone is likely to buy, understand a spoken question, or generate a written response. It can produce results that seem remarkably human, but that does not necessarily mean it thinks, understands, or experiences the world the way a person does.

Things get more confusing when we talk about machine learning. People often use artificial intelligence and machine learning as if they mean the same thing, but machine learning is just one part of AI. And using an AI tool is very different from learning how to build, test, and improve the technology that makes it work.

If you’re thinking about studying AI and machine learning, it helps to start by understanding these differences.

What is artificial intelligence?

Artificial intelligence, or AI, is about building computer systems that can perform tasks that normally require human intelligence.

This includes recognizing images, understanding language, finding patterns, making predictions, solving problems, and helping with decisions.

You likely use AI every day, sometimes without realizing it. Things like recommendation engines, virtual assistants, fraud detection, navigation apps, automated customer service, and generative AI all rely on different forms of artificial intelligence.

AI uses many different methods and technologies. One important example is machine learning.

What is machine learning?

Machine learning is a branch of artificial intelligence. It helps computers find patterns in data and get better at tasks without needing a specific rule for every case.

Instead of making a rule for every decision, developers can create systems that use data to find patterns and make predictions.

For example, a machine learning system can look at thousands of past transactions to spot signs of fraud. Other systems can study medical images, predict when equipment might fail, understand speech, or suggest products based on what someone has chosen before.

In short, artificial intelligence is the bigger field, and machine learning is one way to build it.

AI vs. machine learning: What’s the difference?

Think of AI as a large umbrella that covers many different technologies.

AI includes technologies that help computers do tasks that need some intelligence. Machine learning is a part of AI that focuses on systems learning from data.

Other areas that are part of AI or closely related to it are:

• Computer vision
• Natural language processing
• Robotics
• Information retrieval
• Generative AI
• Intelligent automation
• Deep learning

These technologies often work together. For example, a self-driving car might use computer vision to see its surroundings, machine learning to spot patterns, and AI decision systems to choose what to do next.

That’s why many degree programs teach artificial intelligence and machine learning together. To understand how intelligent systems work, you need to know about computing systems, programming, algorithms, and data.

Do you need programming experience to study AI and machine learning?

The answer depends on which degree and program you choose.

CSU Global’s B.S. in Artificial Intelligence and Machine Learning welcomes students who have no background in AI, programming, or computer science. The program starts with basic programming and computer science classes, then moves on to topics like artificial intelligence, machine learning, computer vision, robotics, cloud-based AI, and other advanced areas.

“The program is designed to make AI and machine learning accessible to students with different levels of technical experience,” said Matthew Brown, Ph.D., program director of computer science at CSU Global. “Courses in Python, Programming I and II, and Data Structures and Algorithms establish the foundation students need to understand how AI systems actually work and to progress successfully into courses such as Artificial Intelligence, Machine Learning, Computer Vision, and Robotics.”

CSU Global’s M.S. in Artificial Intelligence and Machine Learning is meant for students who already have a strong technical or quantitative background and want to build graduate-level skills. The curriculum includes programming, but students learn it as part of a more advanced program that focuses on applying and evaluating AI and machine learning solutions.

What do you learn in an AI and machine learning bachelor’s degree?

A bachelor’s degree in AI and machine learning goes beyond just using current AI tools. You will learn the core principles and technologies that make these tools possible.

CSU Global’s 120-credit bachelor’s program starts with programming and computer science basics, then moves into specialized AI courses. You will study topics such as:

• Programming: Learn how software is designed and developed, including programming with Python.
• Data structures and algorithms: Learn how computers organize, process, search, and use information efficiently.
• Artificial intelligence: Explore how to build systems that can reason, solve problems, and perform smart tasks.
• Machine learning: Discover how systems learn from data, including supervised, unsupervised, and reinforcement learning methods.
• Computer vision: Learn how computers analyze and interpret visual information.
• Robotics: See how software, algorithms, sensors, and automated systems combine to control smart machines.
• Information retrieval: Learn how computers find and rank relevant information, including the technology behind search engines.
• Cloud-based AI: Get hands-on experience with Microsoft Azure and other AI services to build and launch intelligent applications.
• AI ethics and policy: Explore issues like privacy, bias, security, accountability, and the impact of advanced AI on society.
• Applied projects: Use what you’ve learned to create AI and computing solutions for real-world challenges.

By studying these subjects, you will move beyond just using AI tools and start to understand how intelligent systems are designed, built, tested, and used.

What do you learn in an AI and machine learning master’s degree?

A master’s degree builds on your existing technical or quantitative background. At CSU Global, the 30-credit M.S. in Artificial Intelligence and Machine Learning includes courses in programming, software development, algorithms, artificial intelligence, computer vision, and machine learning.

You will learn how to use AI and machine learning to solve real-world problems, develop solutions that model aspects of human behavior, combine different approaches, and test how well your applications work. The program also gives you hands-on experience with Python and deep-learning tools like TensorFlow.

The main difference between the programs is your starting point and how deep you go. The bachelor’s program helps you build a foundation in computer science and programming. The master’s program is for those who are ready to develop advanced technical skills.

Will you study generative AI as part of an AI degree?

Generative AI plays a big role in today’s AI world, but it is just one piece of the bigger picture.

Tools that create text, images, video, audio, or code rely on ideas that go far beyond just giving prompts. To really understand AI, you need to learn about programming, algorithms, data, machine learning, computing systems, and the ethical issues behind smart technologies.

AI tools will keep evolving, but the core principles will stay important for a long time.

“Programming, computational thinking, algorithms, and problem-solving will remain important even as specific AI tools change,” Brown said. “Just as important will be the ability to evaluate AI critically, understand its ethical and social implications, and apply AI and machine learning concepts to real-world problems rather than simply learning how to use today’s tools.”

Is AI the same as data science?

No, they are not the same. While they overlap, each has its own main purpose.

Data science is mainly about collecting, analyzing, and interpreting data to find patterns, answer questions, and help make decisions.

Machine learning uses data to teach systems how to spot patterns and make predictions.

Artificial intelligence is a broader field that aims to create systems able to do tasks that usually require intelligence.

A data scientist might use machine learning, and an AI developer often depends on data. Both roles often use similar programming languages and analytical methods.

These fields are coming together more and more. That’s why skills in programming, math, data, and algorithms can open up many paths in technology.

Is robotics part of artificial intelligence?

Robotics and artificial intelligence are different areas, but they often work together.

Robotics is about building machines that interact with the real world. AI helps these machines understand their surroundings, make choices, and react to changes.

For example, an industrial robot that repeats the same movement over and over might not need much AI. But a robot that can recognize objects, find the best way around obstacles, or change its actions as things change will likely use several AI technologies.

CSU Global’s bachelor’s program covers robotics, as well as computer vision, machine learning, and the programming basics that link these areas.

What’s the difference between an AI and machine learning degree and a computer science degree?

The programs share some of the same foundations, including programming, algorithms, data structures, and software development. The difference is where they go from there.

A computer science degree covers a broad range of computing subjects, which may include operating systems, databases, networks, cybersecurity, and software engineering. An AI and machine learning degree focuses more specifically on intelligent systems, with coursework in machine learning, computer vision, robotics, information retrieval, cloud-based AI, and responsible technology.

Computer science may be the better choice if you want a broad technical education with the flexibility to pursue different areas of computing. An AI and machine learning degree may be a better fit if you already know that you want artificial intelligence to be the primary focus of your studies.

What industries can you work in if you have a degree in AI and machine learning?

People with AI and machine learning skills work in many fields, including health care, finance, manufacturing, retail, logistics, government, marketing, cybersecurity, and software development.

The type of work you do with AI and machine learning depends on the industry. For example, one company might use machine learning to spot fraud, while another might use it to make better predictions, automate tasks, analyze medical images, or control smart machines. Knowing the industry helps professionals pick important problems to solve, use the right data, and understand the rules, risks, and daily challenges that shape how AI is used.

A bachelor’s degree can help prepare you for entry-level roles in AI, machine learning, software development, automation, robotics, cloud computing, and data. If you already have experience, a master’s degree can help you gain deeper skills for more advanced technical roles in AI and machine learning. Keep in mind that having a degree does not guarantee a specific job, since requirements depend on the employer and the position.

How do you choose the best AI and machine learning degree for your needs?

The best degree for you will depend on your education, technical experience, and what you want to achieve.

B.S. in Artificial Intelligence and Machine Learning

CSU Global’s 120-credit bachelor’s degree is for students who want to focus on AI and machine learning during their undergraduate studies. You don’t need any previous experience in programming, computer science, or AI. The program starts with the basics and helps you build a strong foundation before moving on to advanced topics.

M.S. in Artificial Intelligence and Machine Learning

CSU Global’s 30-credit master’s degree is for students with a strong quantitative or technical background who want to expand their skills in programming, algorithms, artificial intelligence, computer vision, and machine learning.

Both programs are fully online, with eight-week, asynchronous courses and no set class times. With monthly start dates, you can begin whenever you’re ready and fit your studies around your job or other commitments.

Learning AI is about understanding what goes on behind the prompt

Knowing how to use AI is now a basic skill in many jobs. But building, testing, and improving AI requires a different set of abilities.

“I hope graduates understand that AI is much more than generative AI or interacting with tools like ChatGPT or Google Gemini; it is a broad field built on computer science, algorithms, data, and different approaches to machine learning,” Brown said. “By graduation, students should understand how to design and implement AI solutions across areas such as computer vision, robotics, information retrieval, and cloud-based AI while also recognizing the ethical and professional responsibilities that come with deploying these technologies.”

If you want to learn what happens behind the prompt, you can start with a bachelor’s degree to learn the basics or choose a master’s degree to build advanced skills in designing, applying, and evaluating AI and machine learning solutions.