Master's in Computer Vision: Guide to Courses, Careers, and Schools

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Turn your love for CV into a lucrative and rewarding career path! Our guide to master’s programs in computer vision and directory of degrees can help you make a quick decision about which MS or MEng to choose. Learn more about the differences between industry-focused and research-oriented graduate offerings in computer vision. Discover what CV courses and electives are available in current MS programs. Opt for a 100% online master’s degree or investigate hybrid and on-campus alternatives. According to Knowledge Sourcing Intelligence, the AI in computer vision market is anticipated to reach a market size of $55.081 billion USD in 2029. With a master’s degree, you’ll be in a prime position for opportunities.

What Is a Master’s in Computer Vision?

Computer vision (CV) aims to replicate human vision capabilities in machines. That means computer vision specialists are fascinated with designing, implementing, and optimizing algorithms and systems that enable machines to interpret and understand visual data (e.g. images, videos, 3D scans, etc.).

Although computer vision is sometimes briefly covered in AI-related bachelor’s degrees, a master’s degree in computer vision goes into far more depth on the topic. A graduate program in CV typically:

Explores areas such as image processing, machine learning, deep learning, 3D reconstruction, object detection and recognition, motion analysis, and visual SLAM.
Puts a heavy emphasis on programming languages such as Python and C++ and frameworks like OpenCV, TensorFlow, and PyTorch.
Includes opportunities for internships, portfolio work, and/or a final capstone project or research-based thesis. You will need these elements to impress employers.

Types of Computer Vision Graduate Degrees

Aspiring computer vision engineers, developers, and researchers have a number of options for graduate study. Master’s degrees and PhD programs will offer the most opportunities for advanced coursework and in-depth research around the field of computer vision.

Master of Science in Computer Vision (MSCV): Schools such as Carnegie Mellon University, the University of Central Florida, and Illinois Tech now offer master’s programs that are completely devoted to computer vision. Once you have completed foundational coursework in areas such as computer vision systems, image processing, visual learning & recognition, digital signal processing, and machine learning, you will be allowed to select CV electives in specific areas of interest (e.g. medical image computing, VR engineering, HCI, etc.).

Master of Science in AI or Machine Learning: Broader degrees like the Master of Science in Artificial Intelligence (MSAI) and the Master of Science in Machine Learning (MSML) can be customized with computer vision electives, but you may be limited to 3-4 courses. For a classic example of this phenomenon, see the University of Maryland’s Master of Science in Applied Machine Learning. It features electives in deep learning, digital signal processing, computer vision, and robotics.

Master of Science in Computer Science (MSCS): A number of universities will allow you to customize your computer science graduate degree with work in computer vision or visual computing. An MSCS may appeal if you’re interested in areas such as algorithms, AI models, software development, and machine learning. Check the curriculum and admissions links in our directory before you apply. Traditional MSCS programs are designed to be research-driven and will often include a thesis as the final project. However, degrees like Columbia and Georgia Tech’s online programs are much more industry-focused.

Master of Science in Electrical and Computer Engineering (MS in ECE) or Electrical Engineering & Computer Sciences (MS EECS): If you wish to investigate how computer vision intersects with areas such as digital signal processing, hardware systems, and embedded computing, then you could consider an ECE or EECS degree with a CV specialization. For examples of this pathway, check out the program links in our directory for schools such as Northwestern and UC Berkeley. MIT also offers EECS options, but the MEng is limited to MIT undergraduates and the SM is designed to be a step towards the doctoral degree.

Master of Science in Robotics (MSR): Those who are passionate about CV applications in robotics should consider customizing their MSR with computer vision electives. For instance, Carnegie Mellon’s research-focused Master of Science in Robotics (MSR) mimics the first two years of a PhD program. And UPenn’s Master of Science in Robotics (MSR) with a specialization in Computer Vision is housed and administered by the prestigious General Robotics, Automation, Sensing, & Perception Lab (GRASP) Lab.

Master of Engineering (MEng) in CS, ECE, or EECS: If you’d like to earn a master’s for professional reasons and you have no intention of focusing on research or proceeding to a PhD, consider the MEng options in our directory. MEng degrees have often been developed as a professional alternative to a traditional, research-heavy MS. An example of this would be UC Berkeley’s MEng in EECS with a specialization in Visual Computing & Computer Graphics. This is a practical and industry-oriented terminal degree.

Admissions Requirements for Computer Vision Graduate Degrees

Undergraduate Degree

Anyone applying to a computer vision graduate degree should have a strong background in computing. Most universities will be expecting master’s candidates to have attained a BS degree in a relevant field. For example:

Applicants to Carnegie Mellon’s MSCV are required to have a BS in engineering, computer science or applied mathematics.
Candidates for the University of Central Florida’s MSCV should have a BS in computer science. However, UCF is willing to consider other applicants who have an understanding of upper-division undergraduate coursework in computer architecture, programming languages, operating systems, and discrete computational structures.

These are examples of programs with a computer science feel. If your MS or MEng in CV is being offered by the School of Engineering, the undergraduate degree requirement will often reflect that bias. For instance:

Applicants to Illinois Tech’s MEng in AI for Computer Vision and Control should ideally have a BS in electrical or computer engineering from an ABET-accredited institution. However, Illinois Tech will consider students with a BS in engineering or science.
Candidates for Northwestern’s MS in Electrical Engineering or Computer Engineering with a specialization in CV and Image Processing should have a BS in electrical engineering, computer engineering, or a related discipline. But highly qualified candidates with other academic backgrounds are welcome to apply.

To help you quickly sort through your options, we have included a link to each program’s admissions requirements in our directory.

Undergraduate Skill Sets

Universities will be looking for computer vision candidates who have key undergraduate skills in programming, operating systems, applied mathematics, and the like. You will usually find these requirements spelled out in the admissions section. Carnegie Mellon even provides a Recommended Skill Set for its MSCV.

Skills needed for entry will increase in proportion to the school’s reputation. For example, in the FAQ for the MSCV, Carnegie Mellon also notes that it hopes to see candidates with hands-on computer vision experience that goes beyond classwork (e.g. internships, research projects, and/or industry experience).

Additional Requirements

In addition to undergraduate transcripts, candidates for master’s programs in computer vision will typically be expected to provide additional materials such as a professional résumé, letters of recommendation, and a statement of purpose. GRE scores may or may not be required—check the admissions links to confirm.

Additional materials may tip the balance in your favor! For instance, Northwestern does not require GRE scores for MS and PhD applicants, but it does put a great deal of weight on the statement of purpose. Candidates must explain why they want to work in the field of computer vision and which faculty member they hope to have as their adviser.

You will put yourself ahead of the pack if you take the time to research the computer vision institutes, centers & labs that are affiliated with the master’s program and reach out to faculty and staff before applying. Examples of CV-related institutions at U.S. universities include:

Carnegie Mellon’s Robotics Institute
Columbia’s Vision, Interaction, Graphics, Robotics (VIGR) Center
The Stanford Vision and Learning Lab (SVL)
Northwestern’s Vision and Graphics research group
UC Berkeley’s work in Computer Vision & AI (CVAI) research
MIT’s Graphics and Vision research group
UPenn’s GRASP Lab
UCF’s Center for Research in Computer Vision (CRCV)

What Will You Learn in a Computer Vision Master’s Program?

Foundational Coursework

Graduate degrees like Carnegie Mellon’s Master of Science in Computer Vision (MSCV) from the Robotics Institute and UCF’s Master of Science in Computer Vision (MSCV) from the Department of Computer Science both begin with fundamental coursework in computer vision, vision systems, and machine learning before proceeding to more advanced subjects.

Popular course topics for a master’s degree in CV might include:

Computer Vision
Image & Video Processing
Machine Learning and Deep Learning for Vision (e.g. CNNs, GANs)
Object Detection, Tracking, and Recognition
3D Reconstruction & Scene Understanding
Visual SLAM (Simultaneous Localization and Mapping)
Video Analytics and Action Recognition
Augmented Reality (AR) & Virtual Reality (VR) Technologies
Edge Computing for Vision Applications
Robotics & Control Systems

However, the curriculum for a master’s degree in computer vision will always be influenced by the school or department that is offering the program. For example, you will notice that Illinois Tech’s MEng in Artificial Intelligence for Computer Vision and Control was developed by the Department of Electrical and Computer Engineering (ECE). That’s why this MEng features core coursework in digital signal processing, control systems, secure machine learning design & applications, and the like.

Computer Vision Electives

Almost every master’s in CV, CS, EECS, AI, ML, Robotics, or a closely related field will allow you to customize your plan of study with specific electives in computer vision or visual computing. Examples of electives in a computer vision graduate degree include:

Medical Image Computing
Vision Sensors
Geometry-based Methods in Vision
Digital Image Processing
Natural & Artificial Vision
Advanced AI
Statistical Machine Learning
Mathematical Introduction to Deep Learning
Autonomous Driving
User Interfaces for Games & Virtual Reality
Robot Localization and Mapping
Intelligent Systems: Robots, Agents, and Humans
Deep Reinforcement Learning for Robotics
Human Computer Interaction (HCI)

This is just a small sample. Use the curriculum links in our directory to view what’s available within the program and to learn more about the strengths of the department that’s offering the degree. Electives should focus on your unique interests.

Master’s Level Skill Sets

The easiest way to determine if a master’s degree in computer vision is relevant is to compare the coursework topics with the skill sets needed for your dream job. For instance, in an analysis of job openings for computer vision engineers, we found that the following skills were in demand:

Programming Languages: Proficiency in Python and C++, as well as Java and MATLAB—fundamental for developing & implementing computer vision algorithms.
Machine Learning & Deep Learning: A strong understanding of machine learning algorithms, neural networks, and statistical models.
Deep Learning Frameworks: Experience with TensorFlow, PyTorch, and Keras—integral to building & training deep learning models for visual data analysis.
Computer Vision Libraries: Familiarity with libraries such as OpenCV, scikit-image, and Pillow—essential for image processing tasks.
Image & Video Processing: Skills in image enhancement, denoising, segmentation, and feature extraction—crucial for interpreting and manipulating visual data.
Advanced Mathematics: Solid foundations in linear algebra, calculus, probability, and geometry—necessary for developing algorithms related to 3D reconstruction, camera calibration, and the like.
Data Analysis & Pattern Recognition: The ability to analyze large datasets (e.g. ImageNet) in order to recognize patterns and extract meaningful insights—vital for training models that can perform accurately.

In addition, computer vision experts will be expected to have fundamental skills in problem-solving & analytical thinking, as well as communication & collaboration.

Hands-On Projects & Research Opportunities

Computer vision is a specialized, hands-on discipline within the field of AI. That means potential employers will be looking for evidence of real-world projects and advanced research on your résumé. That’s why we favor any master’s program in computer vision that incorporates these elements into the curriculum. For example:

Carnegie Mellon’s MSCV features a mandatory summer internship, as well as a two-part capstone project. In the capstone, students are expected to form a small team, tackle a computer vision topic proposed by a faculty member or industry colleague, and grapple with the challenges of real-world software development.
UC Berkeley’s MEng in EECS with a specialization in Visual Computing and Computer Graphics culminates in a two-semester, team-based capstone experience. Teams are expected to produce technical deliverables (e.g. prototypes), project management & teaming deliverables (e.g. project charter), and reporting deliverables (e.g. final presentation).
Northwestern’s more research-focused MS in Electrical Engineering or MS in Computer Engineering with a specialization in Computer Vision and Image Processing gives MS students the option to complete a research project, a thesis, or a coursework-only track.

Even if the degree does not include a dedicated capstone project or credits in independent research, you can still make the most of your experience.

Volunteer to assist faculty on their current CV research projects.
Ask the university’s computer vision labs & institutes about opportunities.
Create your own CV portfolio using skills learned in your courses.
Read the latest research papers & try to replicate their results.
Submit your original CV research to journals and conferences.

Online Master’s in Computer Vision Programs

It is possible to earn an online master’s degree in computer vision. However, your choices may be limited to majors that offer computer vision as a specialization. Programs that are completely devoted to computer vision—including Carnegie Mellon’s MSCV and UCF’s MSCV—are typically campus-based.

If you’re unable to choose an in-person or hybrid degree, here are two 100% online master’s programs that explore computer vision in some shape or form:

Columbia’s Online Master of Science in Computer Science (MSCS) – Vision, Graphics, Interaction and Robotics allows you to select two track courses in computer vision and two electives in subjects such as biometrics, advanced machine learning, HCI, and robotics.
Georgia Tech’s renowned Online Master of Science in Computer Science (OMSCS) has a specialization in Computational Perception and Robotics, with core courses in algorithms, AI, and machine learning, as well as electives in perception.

You could also consider testing the waters before you commit to a master’s degree. For example:

Stanford’s Online Graduate Certificate in Visual Computing includes a mandatory seminar in image systems engineering and a choice of four graduate-level courses in computer vision, computer graphics, and computational imaging. Up to 18 units can be applied to a Stanford master’s degree program.
Penn State’s Online Graduate Certificate in Computer Vision features three courses in reinforcement learning, machine vision, and data visualization. Credits from this online certificate can be applied to the 100% Online Master of Artificial Intelligence.

Industries Requiring Computer Vision Graduates

Computer vision (CV) skills are in high-demand across a wide variety of industries. Medical imaging is one of the most well-known applications, but computer vision crops up in some surprising areas. Examples of fields that require CV expertise include:

Agriculture & AgTech: Crop disease detection, yield estimation, livestock monitoring, and autonomous farming.
Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR): Spatial mapping, eye tracking, real-time object recognition, and avatar facial expressions.
Autonomous Vehicles & Advanced Drive Assistance Systems (ADAS): Object detection, lane detection, pedestrian tracking, 3D mapping, and visual odometry.
Consumer Electronics & Mobile Technologies: Face recognition, gesture control, AR filters, biometric authentication, and scene understanding in cameras.
Defense & Aerospace: Target recognition, drone vision, terrain mapping, and autonomous navigation.
Healthcare & Medical Imaging: Automated diagnostics, surgical navigation, histopathology analysis, and retina imaging.
Manufacturing & Industrial Automation: Quality inspection, defect detection, predictive maintenance, and robotic arm guidance.
Retail & E-Commerce: Visual search, shelf stock monitoring, customer behavior tracking, and cashier-less checkouts.
Security, Surveillance & Smart Cities: Facial recognition, anomaly detection, crowd counting, license plate recognition, and traffic monitoring.

Common Computer Vision Job Titles

One of the most common job titles for a computer vision specialist is a computer vision engineer. However, you’ll find plenty of CV-related roles when you search for this title on popular job sites. To get a sense of the market and employer needs, have a look at current job descriptions for the following roles:

Computer Vision Engineer: Developing and optimizing algorithms that enable machines to interpret visual data, often working with technologies like deep learning, 3D reconstruction, and real-time object tracking.
Applied AI Engineer (Computer Vision): Applying AI techniques to practical computer vision problems, bridging the gap between research and product development.
Machine Learning Engineer (Computer Vision): Applying machine learning techniques to CV tasks such as image recognition and object detection.
Software Engineer (Computer Vision): Developing software solutions & intelligent rich perception systems that incorporate CV capabilities.
Research Scientist (Computer Vision): Conducting advanced research to innovate and improve computer vision technologies; often requires a PhD and a track record of scholarly articles.

Computer vision experts can also consider applying for broader AI, machine learning, and robotics roles that require vision-specific skills. Examples of these types of jobs include:

AI/ML Engineer: Designing and deploying AI and machine learning models to solve complex problems across domains like vision.
Machine Learning Developer: Implementing, training, and optimizing machine learning algorithms within software systems to enable predictive and adaptive capabilities.
AR/VR Developer: Building immersive augmented and virtual reality experiences using 3D engines and sensor data, often incorporating computer vision and spatial computing.
Robotics Engineer: Designing, building, and testing robotic systems and hardware for tasks such as automation, navigation, and manipulation in physical environments.
Robotics Software Developer: Programming control systems and perception algorithms that allow robots to interpret data and perform autonomous actions.

FAQ

What’s the Difference Between a Master’s in Computer Vision and a Master’s in Visual Computing?

Our program directory contains links to master’s degrees in computer vision and master’s degrees in visual computing. So you may be wondering which pathway to choose.

Master’s in Computer Vision: Focused on teaching machines to interpret & understand visual data and aligned with AI, machine learning, and robotics applications. Core topics covered may include image & video processing, machine learning & deep learning for vision (e.g. CNNs, GANS, etc.), object detection & tracking, 3D reconstruction, Visual SLAM, real-time video analytics, and more.
Master’s in Visual Computing: A broader examination of how computers generate, process, analyze, and visualize visual information, including both perception and rendering. Core topics covered may include generative & interactive visual systems, computer vision, computer graphics & rendering, visualization, AR & VR, HCI, and image synthesis & simulation.

Do I Need a Master’s in Computer Vision to Land a Job?

If you are applying for a role that requires advanced skills in computer vision, machine learning, and deep learning, you should have a master’s degree in a field such as computer vision, computer science, ECE, or a related field. Here’s why:

A few employers may be willing to consider candidates with a BS if they have 5+ years of relevant experience in computer vision, but the vast majority will state that a master’s or PhD is the preferred qualification. That means anyone with a bachelor’s degree will be competing against better-qualified applicants.
Openings for research-intensive positions such as a computer vision research scientist will usually specify that candidates have a PhD. The same holds true for any role in academia. If you’re interested in research roles, remember that some MS programs are designed to help you transition into a doctorate.

Are you trying to acquire real-world experience and build up your portfolio of CV projects before you tackle a master’s degree? Some computer vision professionals with a BS have been able to break into the field by applying for positions in startups. These companies are generally less picky than big tech employers. They may also be willing to help subsidize your graduate certificate or degree.

Do I Need to Earn a Master’s in Computer Vision from a Top-Tier University?

There’s no doubt that a master’s degree in computer vision from a prestigious university like Carnegie Mellon, UC Berkeley, or Stanford will help you impress employers. View a list of the top 25 universities for AI to find out which schools are regarded as the best of the best.

Having said that, it can be extremely challenging to get into one of these graduate programs. You may find that you are simply not qualified to apply. Or you may decide that the computer vision coursework leans too heavily toward engineering or robotics for your liking.

If this is the case, you may be better off opting for a lesser known university that has amazing CV faculty members and active research labs. As master’s alumni have pointed out, sometimes you end up getting more personalized attention at “second-tier” schools. Professors at top schools are often focused on their PhD students.

How Can I Fund a Master’s Degree in Computer Vision?

Master’s degrees in computer vision can be industry-focused or research-focused.

Industry-Focused Master’s Programs: Typically regarded as standalone, terminal qualifications that do not lead to any further study. Students in these programs are often expected to support their studies through loans, personal savings, and/or employee tuition reimbursement. For example, students in Carnegie Mellon’s MSCV are not eligible to receive any financial aid or scholarship funding and must be fully self-supported.
Research-Focused Master’s Programs: Usually regarded as the first step towards further research work. For instance, UC Berkeley’s MS in EECS is designed to lead to a career in industrial R&D or a PhD. Students in these programs may be eligible for tuition support, living stipends, graduate fellowships, teaching & research assistantships, and other types of institutional aid.

What If I’m Not Ready for a Master’s in Computer Vision?

It’s important to note that a number of prestigious universities offer online graduate certificates in computer vision, as well as non-credit training programs. Offerings like Udacity’s Nanodegree in Computer Vision can also give you a taste of the topic and help you build a portfolio of projects.

Master’s in Computer Vision Program Directory

We’ve done the legwork to provide you with a directory of every accredited master’s degree in computer vision available in the USA! To qualify for inclusion, universities had to be regionally accredited and offer programs with dedicated majors or specializations in computer vision or visual computing. Our directory includes a wide mix of on-campus and online degrees so you can find the option that fits you best.