Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based |
Course Overview
Computer vision is a rapidly growing field with applications in a wide range of industries. The Computer Vision certification by Carnegie Mellon University's School of Computer Science is a 10-week long online certification course that provides a comprehensive introduction to the theory and algorithms used in computer vision systems. Emeritus along with CMU is providing this course. Computer Vision covers a wide range of topics, including image detection, recognition and processing, 3D reasoning, and video analysis. It also provides insights into the real-world uses of computer vision technology.
The Computer Vision certification is ideal for technology professionals who want to learn more about computer vision and how it can be used in their work. It is also a good fit for machine learning engineers who want to gain hands-on experience with computer vision tools and techniques.
The Computer Vision certification course requires knowledge of multivariable calculus, linear algebra, probability, and statistics, as well as Python programming. This course is taught by leading experts in the field of computer vision. Additionally, the course includes a capstone project that allows students to apply their skills to real-world problems.
The Highlights
- 10 weeks online course
- Offered by Carnegie Mellon University
- Programming Assignments
- Problem-Based Learning
Programme Offerings
- Certificate of completion
- Online Classes
- Program exercises
- Knowledge checks
- Support team
- Discussion boards
- flexible schedule
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | CMU Pittsburgh |
The Computer Vision certification fee is US$2,500. Flexible payment options are available; you can pay the course fee all at once or in instalments.
Computer Vision Certification Fee Structure
Particulars | Total Fee |
Pay in full | US$2,500 |
Pay in 2 instalments | US$2,613 |
Pay in 3 instalments | US$2,688 |
Eligibility Criteria
Certification Qualifying Details
To obtain the Computer Vision certification by Carnegie Mellon University’s School of Computer Science Executive & Professional Education, you must complete all ten modules of this course along with the assessments successfully.
What you will learn
After completing the Computer Vision certification syllabus, you will learn the fundamental image processing methods, neural networks for image recognition and classification, 3D information extraction, and object alignment and tracking in video.
Upon completion of the Computer Vision training, you will explore real-world applications of computer vision technology. You will be able to apply your knowledge to real-world problems through hands-on projects.
Who it is for
The Computer Vision online course is designed for programmers with experience in Python who want to learn about the rapidly evolving field of computer vision. This course is particularly useful for
Admission Details
To join the Computer Vision classes, aspirants can follow these steps:
Step 1: Go through the URL below: https://execonline.cs.cmu.edu/computer-vision
Step 2: Fill out the relevant details
Step 3: Download the brochure
Step 4: Go through the course page and click on the Apply Now tab
Step 5: Fill out the application form online and pay the course fee
Application Details
You must complete the online application form to enrol in the Computer Vision certification course.
The Syllabus
- Define the core concepts of computer vision
- Identify the real-world applications of the technology
- Describe the basic principles of image processing
- Learn the various techniques used for image filtering and decomposition
- Explore the essential feature-detection methods
- Use the methods to build and train algorithms to detect corners and to visualize quadratics in images
- Create image representation with features using the Bag-of-Visual-Words concept
- Use neural networks to classify images
- Describe the structure and function of a CNN
- Use a deep CNN to recognize objects in an image
- Apply 2D planar and linear transformations to given images.
- Perform automatic image warping and basic augmented-reality simulations
- Describe geometric camera models
- Calibrate a camera
- Learn the basic principles of geometry-based vision
- Reconstruct 3D scene structures from two images
- Perform robust 3D sensing using stereo
- Study the applications of optical flow
- Track objects in a video
- Describe how physics-based vision interprets and extracts information from an image
- Perform photometric stereo and render simple images
CMU Pittsburgh Frequently Asked Questions (FAQ's)
There are ten modules in this course that are covered in 10 weeks if you give 5-10 hours to it every week.
The course takes place in online mode, allowing you to take the classes at your own pace.
This online course is available on Emeritus which is offered by Carnegie Mellon University’s School of Computer Science Executive & Professional Education
To excel in this course, you must have some experience in programming in Python and a functional knowledge of linear algebra, calculus, probability, and statistics.
This course is designed for programmers with experience in Python who want to learn about the rapidly evolving field of computer vision.
There are ten modules in this course that are covered in 10 weeks if you give 5-10 hours to it every week.
The course takes place in online mode, allowing you to take the classes at your own pace.
This online course is available on Emeritus which is offered by Carnegie Mellon University’s School of Computer Science Executive & Professional Education
To excel in this course, you must have some experience in programming in Python and a functional knowledge of linear algebra, calculus, probability, and statistics.
This course is designed for programmers with experience in Python who want to learn about the rapidly evolving field of computer vision.