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The use of AI is becoming integrated into fields ranging from computer science and medicine to education, design, and scientific research. Online courses can help high school students build their interests and foundational knowledge if they want to explore AI before choosing a college major.
One place to start exploring AI is MIT OpenCourseWare (OCW), which is an online platform that offers free access to learning materials from more than 2,500 MIT courses. The materials may include recorded lectures, notes, assignments, programming exercises, and project descriptions, depending on each course. Most OCW materials can be accessed without registration, which allows students to study on their own.
However, not every AI course is suitable for beginners. Some courses introduce the basic concepts without requiring programming knowledge, while others assume one’s familiarity with Python, calculus, or linear algebra. A course designed for graduate students may also be challenging, even if its materials are accessible online.
This article introduces seven options covering different interests and experience levels, from understanding generative AI to building and training machine learning models. Rather than trying to complete all seven of them, you can choose a course that matches your current skills and gives you a clear next step to take.
7 Free MIT AI Courses at a Glance
| Course: | Main Focus: | Recommended Background: | Best for: |
| AI 101 | AI fundamentals | Little to no prior experience | Students exploring AI for the first time |
| 6.S087: Foundation Models and Generative AI | Generative AI and foundation models | No technical background required | Students curious about ChatGPT and modern AI |
| 6.036: Introduction to Machine Learning | Machine learning algorithms | Python, calculus, and linear algebra | Students ready for technical study |
| 6.S191: Introduction to Deep Learning | Neural networks and modern deep learning | Basic calculus and linear algebra; Python (helpful) | Students interested in hands-on AI |
| 6.034: Artificial Intelligence | Classical AI and problem-solving methods | Some programming experience | Students seeking a broader technical foundation |
| 6.S062: Generative AI in K–12 Education | Generative AI and educational applications | Interest in AI and education; project work may require additional skills | Students interested in education technology |
| MAS.S60: How to AI (Almost) Anything | Multimodal AI and research | Prior AI exposure recommended | Students ready to explore advanced applications |
Take note that these are free learning resources, not online certificate programs. Some of them provide a structured sequence of lectures and assignments, while others offer lecture materials or workshop content. Gaining access to these materials does not automatically include an MIT enrollment, academic credit, instructor feedback, or a certificate.
AI 101: A Starting Point for Students With No AI Background
Best for: High school students who want to understand what artificial intelligence does before committing to a more technical course.
Course format: Introductory workshop with video materials and supporting resources.
AI 101 is an introductory workshop created by MIT researcher Brandon Leshchinskiy for learners having little or no prior experience. Rather than beginning with advanced mathematics, it introduces concepts that help students understand how AI systems work and what problems they can solve.
Topics include machine vision, data wrangling, and reinforcement learning. This workshop also includes an interactive exercise where learners can train an algorithm, offering a first look at how an AI system can learn from data.
This is a useful first step for students who have used AI tools but have not studied the underlying technology. It can also help students decide whether they are more interested in AI’s technical foundations, real-world applications, or broader social implications.
Because AI 101 is a short workshop, it should be viewed as an introduction, not comprehensive technical training or a full semester-long course.
What to do next: After completing the workshop, you can choose a more focused direction. Students who want to understand modern generative AI can continue with 6.S087. At the same time, those interested in building models may eventually progress to machine learning or deep learning.
6.S087: Foundation Models and Generative AI
Best for: Students who want to understand the technology behind ChatGPT and other generative AI systems without starting with extensive programming.
Course format: Nontechnical lecture series.
Using a chatbot is not the same as understanding how it works. MIT’s 6.S087 introduces the ideas behind foundation models and generative AI, helping learners explore the rapid development of these technologies and their uses.
This course begins with a short history of AI and examines the limitations of earlier approaches. It then introduces concepts such as supervised learning, reinforcement learning, and self-supervised learning, which can help explain how modern foundation models are developed.
Some examples include ChatGPT, Microsoft Copilot, image-generation systems such as DALL·E and Stable Diffusion, AlphaFold, and self-driving cars. These examples show how related AI techniques apply to different problems.
Unlike a programming-intensive course, 6.S087 is designed for learners with a wide range of backgrounds. This course is for students interested in AI’s influence on science, business, and society, even if they are not yet sure whether they want to study computer science.
What to do next: You can choose a real-world AI application and investigate how it works, what problems it solves, and its limitations. A short research project comparing different applications can help turn your general curiosity into a more specific academic interest.
6.036: Introduction to Machine Learning
Best for: Students with programming and math experience who want to learn how machine learning models make predictions.
Recommended prerequisites: Python, calculus, and linear algebra.
Machine learning is a central area of AI that focuses on developing systems that learn patterns from data. MIT’s 6.036 introduces fundamental principles, algorithms, and applications through modeling and prediction.
Rather than focusing only on existing AI tools, this course examines how learning problems are formulated and how data is represented. Students encounter important concepts such as overfitting and generalization, which help explain why a model may perform well on its training data but struggle with unfamiliar examples.
The course also includes lectures, notes, exercises, labs, and homework. The Open Learning Library version lists a 13-week structure and an estimated workload of about 12 hours per week, so students should plan their schedule for a meaningful time commitment.
This platform is not the best place to start if you have never programmed or studied the relevant math. Students who need to build these foundations first may find it more productive to work through introductory Python and mathematics before attempting the assignments.
The Open Learning Library version also states that certificates cannot be earned through that platform.
What to do next: After learning the basics, you can apply one of the course’s ideas to a small dataset. For example, you could investigate how a model makes its predictions, compare its performance under different conditions, and explain where its predictions become unreliable.
6.S191: Introduction to Deep Learning
Best for: Students who want hands-on experience with neural networks and modern AI applications.
Recommended prerequisites: Basic calculus and linear algebra. Python experience is helpful.
Deep learning uses neural networks to learn complex patterns from data. It underlies many modern AI applications, including image recognition, language processing, generative models, and aspects of scientific research.
MIT’s 6.S191 is an introduction to these techniques, combining lectures with software labs and project work. This course covers areas such as deep sequence modeling, computer vision, generative modeling, reinforcement learning, and emerging applications.
The 2026 edition ran from March 30 through May 25, with lectures and software labs released online during the course. The official website provides access to the 2026 schedule and materials, while maintaining links to earlier editions.
Some labs involve practical exercises such as music generation, facial detection, and fine-tuning a large language model. These activities give students an opportunity to move beyond reading about AI and experiment with its underlying techniques.
Although the course welcomes learners from outside computer science, it has an intensive introduction rather than a no-prerequisite course. Students should be comfortable with basic matrix multiplication, derivatives, and the chain rule. Python experience is helpful but not required.
What to do next: You can complete a lab and then modify one part of the experiment. For instance, change an input, compare outputs, or investigate a limitation. Documenting what you changed and what you learned can turn a course exercise into a more substantial learning experience.
6.034: Artificial Intelligence
Best for: Students who want to understand classical AI methods and how computers approach problems that require reasoning or decision-making.
Recommended background: Some programming experience.
Modern AI conversations often focus on large language models, but many important AI ideas predate today’s generative systems. MIT’s 6.034, taught in fall 2010 by Professor Patrick Henry Winston, introduces foundational approaches to knowledge representation, problem-solving, and learning.
Topics include search algorithms, game-playing strategies, constraint satisfaction, decision trees, neural networks, probabilistic inference, and other classical methods.
Because this version of the course dates back to 2010, it does not cover today’s transformer-based large language models in the way a recent course would. Its value lies elsewhere: it helps students understand the kinds of computational problems AI researchers have long tried to solve and the different approaches to solve them.
This course provides lecture videos, problem-solving sessions, programming assignments, quizzes, and exams. MIT’s instructor guidance warns students without programming experience not to take the subject, since much of its homework involves writing or understanding small programs.
For students who have already learned some Python, 6.034 can provide a broader foundation for thinking about AI. It can also complement a modern deep learning course, showing learners how contemporary methods relate to earlier approaches.
What to do next: You can pick one classical technique, such as search or a decision tree, and implement a small example. Then, compare its strengths and limitations with a modern AI tool that addresses a similar problem.
6.S062: Generative AI in K–12 Education
Best for: Students interested in AI, educational technology, learning science, or how schools might use emerging technologies.
Course format: Lectures, notes, assignments, podcasts, and project-oriented materials.
Not every AI course needs to focus on developing algorithms. MIT’s 6.S062 examines how generative AI could affect teaching and learning, connecting technical concepts with questions about classroom practice and educational design.
This course introduces the foundations of generative AI and discusses how these systems can produce text, images, music, video, and code. It also asks students to think critically about technology’s capabilities, limitations, and sometimes exaggerated claims.
The course materials include contributions from faculty and educators across MIT’s computer science and media arts communities, including Professor Harold Abelson and Professor Cynthia Breazeal. The course’s original work was mostly project-based, involving the design and testing of educational tools with K–12 students and teachers.
High school students might want to evaluate whether an AI-powered learning tool genuinely helps students: Does it improve one’s understanding? Does it encourage their independent thinking? How should its performance be measured?
This course’s original projects may assume resources or collaboration that an independent learner does not have. Students can still explore the lecture materials and assignments, but they should distinguish between studying the content and reproducing the full original course experience.
What to do next: You can investigate one educational problem, such as helping students practice a language or receive feedback on writing. Compare an AI-based approach with a traditional one, and consider how you would test whether the AI tool actually improves learning.
MAS.S60: How to AI (Almost) Anything
Best for: Students who already have some AI experience and want to explore multimodal systems or research-oriented applications.
Course level: Graduate Level
Many AI systems work primarily with one type of input, such as text or images. Multimodal AI aims to connect different forms of information, allowing systems to work across language, images, audio, sensors, medical data, music, and other modalities.
MIT’s MAS.S60 explores these applications through modern deep learning and foundation models. The spring 2025 course, taught by Professor Paul Liang, includes lectures, readings, discussions, and a substantial research component.
Students are encouraged to consider how AI can be applied to unfamiliar data types and real-world problems. This makes the course particularly relevant to learners interested in interdisciplinary research, where computer science connects with medicine, art, music, or human-computer interaction.
The course specializes in graduate-level designation. Its materials are freely available, but the content and research expectations may be demanding for high school students who have not yet developed a technical foundation.
Rather than treating it as a first course, consider MAS.S60 after gaining experience with programming, machine learning, or deep learning.
What to do next: You can explore a question that combines AI with another subject you care about. For example, you might investigate how computer vision can help analyze artwork or how audio models can identify patterns in music. Start with a manageable question rather than attempting to reproduce an entire graduate-level research project.
Which MIT AI Course Should You Take First?
You do not need to complete all seven courses. A focused learning path is generally more useful than collecting random course titles without fully mastering the material.
If you are new to AI:
Start with AI 101, followed by 6.S087: Foundation Models and Generative AI. Together, they provide an accessible introduction to AI concepts and modern generative systems without requiring you to be familiar with advanced programming.
Once you understand the basic ideas, decide whether you want to pursue technical study or explore an application area such as education, business, or science.
If you know Python and have a math foundation:
Consider 6.036: Introduction to Machine Learning or 6.S191: Introduction to Deep Learning.
Choose 6.036 if you want a structured introduction to machine learning concepts, including modeling, prediction, and generalization. Choose 6.S191 if you are especially interested in neural networks and hands-on experiments with modern deep learning.
You can study both eventually, but make sure to check the prerequisites and workload before committing to a full sequence.
If you want a broader understanding of AI:
Add 6.034: Artificial Intelligence to your learning plan. Its older material can help you understand classical AI methods and the evolution of the field.
This course is most useful when you already have some programming experience and want to go beyond the applications that dominate current AI news.
If you are interested in AI research or interdisciplinary work:
Consider MAS.S60 after developing stronger technical skills. If your interests revolve around teaching, learning, or educational technology, 6.S062 may be the right course for you.
You do not need to study every branch of AI to develop a meaningful interest in one of them. The right choice depends on what question you want to investigate.
How High School Students Can Get More Out of Free MIT Courses
Completing an online course can help you explore a subject, but watching lecture videos alone may not show how well you understand the material or whether you want to pursue it further. A more useful approach is to connect learning with practice.
Set a realistic goal. Decide whether you want to understand AI concepts, learn a programming technique, or investigate a particular application. Setting a clear goal makes it easier for you to choose the right course and avoid taking on more material than you can manage.
Work through the exercises. When assignments or labs are available, try solving them before looking at solutions. If you get stuck, identify the specific concept you do not understand and review that material.
Build something small. A simple experiment, data analysis, or educational prototype can help you apply what you have learned. The project does not need to be groundbreaking. Instead, it should demonstrate your thinking and show how well you used the course material.
Document your process. Take note of the question you explored, the methods you tried, what worked, what failed, and what you would change next. This record can also help you decide whether to pursue a more advanced course or research question.
Follow your interests. If a particular topic captures your attention, you can explore it in greater depth, rather than switching immediately to another course. Sustained curiosity often leads to asking better questions and creating more meaningful work.
For students considering computer science, AI, or data science in college, this process can help clarify their academic interests. It can also build a foundation for future projects, independent research, or related extracurricular activities.
Are MIT OpenCourseWare Courses Worth It for College Applications?
Free MIT course materials can be a valuable way for students to explore a possible major, but completing a course does not automatically strengthen their college application.
Admissions officers evaluate academic interests in the context of the student’s broader record. An online course may help demonstrate initiative, but its significance depends on what the student learns and how that learning connects to their interests.
For example, a student who watches several AI lecture series but cannot explain what they learned may have relatively little to say about the experience. Another student who completes one course, develops a small project, identifies its limitations, and uses the results to pursue a more specific question may gain a deeper understanding of the field.
There is no requirement to turn every interest into a research project or competition. Sometimes the most useful outcome is discovering that a subject is not the right fit. The goal is for students to explore honestly and develop their skills over time, not to collect prestigious names for an activities list.
For high school students, the best starting point is often one course, a realistic study schedule, and a question they genuinely want to answer. From there, they can naturally develop further studies.
If you are planning for college and want to explore how academic interests can develop into meaningful projects, research, or extracurricular activities, contact Ivy Talent Education to discuss your goals and possible next steps.
