The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
Master the core computer vision skills advancing robotics and automation
TBD
10 weeks, online
5-10 hours per week
Our participants tell us that taking this program together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistBased on the information you provided, your team is eligible for a special discount, for Computer Vision starting on TBD .
We’ve sent you an email with enrollment next steps. If you’re ready to enroll now, click the button below.
Have questions? Email us at group-enrollments@emeritus.org.With advances in machine learning (ML), the field of computer vision and its applications are growing by leaps and bounds, triggering transformations across industries and in daily life. Computer Vision is an online program offered by the Executive Education division of Carnegie Mellon University’s School of Computer Science. It enables software developers, ML engineers, and technology professionals to expand their knowledge with computer vision and image processing skills to become truly future-ready.
Source: U.S. News & World Report
This is a 10-week online program designed to provide software developers, technology professionals, data scientists, data analysts, and ML professionals with an understanding of computer vision concepts, tools, and techniques. The program also explores real-world applications of this technology. In this program, you will:
The program comprises 10 modules designed to help you leverage your Python skills and mathematical knowledge to gain deep insights into computer vision and image processing concepts.
The program begins by clearly defining the core concepts of computer vision and identifying real-world applications of this technology.
Explore the basic principles of image processing and learn the various techniques used for image filtering and decomposition.
Feature detection is a cornerstone of computer vision. Explore essential feature-detection methods, and use them to build and train algorithms to detect corners and visualize quadratics in images.
Leverage your experience in ML to create image representations with features using the Bag-of-Visual Words concept. Learn to use neural networks to classify images.
Learn about the structure and function of CNNs, using a deep CNN to recognize objects in an image.
Learn to apply 2D planar and linear transformations to given images, the process of performing automatic image warping, and explore basic augmented-reality simulations.
Learn the basics of geometric camera models and how to calibrate a camera.
Discover the basic principles of geometry-based vision, learn to reconstruct 3D scene structures from 2D images, and perform robust 3D sensing using stereo.
Study the applications of optical flow and track objects in a video.
Understand the function of physics-based vision in interpreting and extracting information from an image, and perform photometric stereo for rendering simple images.
The program begins by clearly defining the core concepts of computer vision and identifying real-world applications of this technology.
Learn to apply 2D planar and linear transformations to given images, the process of performing automatic image warping, and explore basic augmented-reality simulations.
Explore the basic principles of image processing and learn the various techniques used for image filtering and decomposition.
Learn the basics of geometric camera models and how to calibrate a camera.
Feature detection is a cornerstone of computer vision. Explore essential feature-detection methods, and use them to build and train algorithms to detect corners and visualize quadratics in images.
Discover the basic principles of geometry-based vision, learn to reconstruct 3D scene structures from 2D images, and perform robust 3D sensing using stereo.
Leverage your experience in ML to create image representations with features using the Bag-of-Visual Words concept. Learn to use neural networks to classify images.
Study the applications of optical flow and track objects in a video.
Learn about the structure and function of CNNs, using a deep CNN to recognize objects in an image.
Understand the function of physics-based vision in interpreting and extracting information from an image, and perform photometric stereo for rendering simple images.
Office Hours With Learning Facilitators
Programming Assignments
Recorded Videos
Knowledge Checks
Dedicated Program Support Team
Discussion Boards
Bite-Sized Learning
This program is designed for participants who have programming experience in Python, and knowledge of multivariable calculus, linear algebra, probability, and statistics. The program is most suitable for:
Software developers/technology professionals who want to get a deep understanding of computer vision tools and advance their career with a certificate from a renowned school.
Representative roles include:
Data science/data analytics/machine learning (ML) professionals looking to improve their knowledge of computer vision technologies and their applications across industries.
Representative roles include:
PREREQUISITES: This program requires a functional knowledge of linear algebra, calculus, probability, and statistics. Participants should be comfortable programming in Python. Programming assignments will present opportunities to implement computer vision algorithms using these technologies.
Kris Kitani
Ioannis Gkioulekas
Kris Kitani
Associate Research Professor, Robotics Institute, School of Computer Science Courtesy Professor, Electrical and Computer Engineering Department, Carnegie Mellon University
Kris Kitani works in the areas of computer vision, machine learning and human-computer interaction. His research interests lie at the intersection of first-person vision, human activity modeling, and inverse reinforcement learning. His work has applications spanning personal and assistive robotics, surveillance and security, infrastructure, field robotics, and manufacturing. Kitani earned his Ph.D. and master’s degree in science from the University of Tokyo. He also has a bachelor’s degree in science from the University of Southern California.
Ioannis Gkioulekas
Assistant Professor, Robotics Institute, Carnegie Mellon University
Ioannis Gkioulekas works on computational imaging — the process of forming images from measurements using algorithms that rely on a significant amount of computing. While imaging involves optics, sensors, and illumination, computation includes physics-based modeling and rendering, inverse algorithms, and learning. Gkioulekas’ research interests include imaging around walls or through skin, lightweight depth sensing, material acquisition, and adaptive imaging. He is also broadly interested in computer vision and computer graphics.
Gkioulekas earned his Ph.D. from the School of Engineering and Applied Sciences at Harvard University. He also has a Diploma in Electrical and Computer Engineering (five-year degree) from the National Technical University of Athens.
Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University’s School of Computer Science Executive Education. This is a training program and it is not eligible for academic credit.
Download BrochureYour digital certificate will be issued in your legal name and emailed to you at no additional cost, upon completion of the program, per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of Carnegie Mellon University’s School of Computer Science Executive Education.