The University of Pennsylvania is offering free online course on Robotics: Vision Intelligence and Machine Learning. Students will understand how Machine Learning extracts statistically meaningful patterns in data that support classification, regression and clustering.
In this twelve week course, applicants will learn how to design robot vision systems that avoid collisions, safely work with humans and understand their environment. This course will start on July 10, 2017.
Course At A Glance
Length: 12 weeks
Effort: 8-10 hours pw
Institution: University of Pennsylvania and edx
Certificate Available: Yes, Add a Verified Certificate for $349
Session: Course Starts on July 10, 2017
The University of Pennsylvania is an Ivy League institution with 12 undergraduate, graduate and professional schools in Philadelphia, serving a diverse community of more than 20,000 students from around the world.
About This Course
Projects in this course will utilize MATLAB and OpenCV and will include real examples of video stabilization, recognition of 3D objects, coding a classifier for objects, building a perceptron, and designing a convolutional neural network (CNN) using one of the standard CNN frameworks.
Why Take This Course?
This course, part of the Robotics MicroMasters program, you will be able to program vision capabilities for a robot such as robot localization as well as object recognition using machine learning.
- The fundamentals of image filtering and tracking, and how to apply those principles to face detection, mosaicking and stabilization
- How to use geometric transformations to determine 3D poses from 2D images for augmented reality tasks and visual odometry for robot localization
- How to recognize objects and the basics of visual learning and neural networks for the purpose of classification
Jianbo’s group is developing vision algorithms for both human and image recognition.
Kostas’ research interests are in computer vision and robotic perception. His research addresses challenges in the perception of motion and space, such as the geometric design of cameras, and the interplay of geometry and appearance in perception tasks.
Dan’s research focuses on applying knowledge about biological information processing systems to building better artificial sensorimotor systems that can adapt and learn from experience.
- College-level introductory linear algebra (vector spaces, linear systems, matrix decomposition)
- College-level introductory calculus (partial derivatives, function gradients)
- Basic knowledge of computer programming (variables, functions, control flow) is preferred, but students may also choose to learn it on their own. The class projects will be carried out MATLAB/Python, with C++ as an option.
How To Join This Course
- Go to the course website link
- Create an edX account to SignUp
- Choose “Register Now” to get started.
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