Graduate

15-862 Computational Photography

Instructor: Kris Kitani

University Units: 12.0

Semester Offered: Fall More

11-776 Human Comm. and Multimodal Learning

Instructor: Louis-Philippe Morency

University Units: 12.0

Semester Offered: Fall More

10-725 Convex Optimization

Instructor: Ryan Tibshirani

University Units: 9.0,12.0

Semester Offered: Fall and Spring More

10-708 Probabilistic Graphical Models

Instructor: Poe Xing

University Units: 12.0

Semester Offered: Spring More

10-702 Statistical Machine Learning

Instructor: Larry Wasserman and Ryan Tibshirani

University Units: 12.0

Semester Offered: Spring More

18799J Compressive Sensing and Sparse Optimization

Instructor: 

University Units: 12.0

Semester Offered:  More

16-831 Statistical Techniques in Robotics

Instructor: Kris Kitani and Michael Kaess

University Units: 12.0

Semester Offered: Fall More

16-899 Actuation and Sensing Mech. for Rob. Sys.

Instructor: Yong-Lae Park

University Units: 12.0

Semester Offered: Fall More

16-627 MSCV Seminar

Instructor: Abhinav Gupta and Srinivasa Narasimhan

University Units: 0.0

Semester Offered: Fall More

16-822 Geometry-based Methods in Vision

Instructor: Martial Hebert

University Units: 12.0

Semester Offered: Spring More

Undergraduate

16-385 Computer Vision

Instructor: Kris Kitani

University Units: 9.0

Semester Offered: Spring More

15-462 Computational Photography

Instructor: Kris Kitani

University Units: 12.0

Semester Offered: Fall More

Masters in Computer Vision Program

APPLY NOW!

Computer vision is the study of acquiring and interpreting visual imagery. With the exponential growth of digital images and videos captured by cameras the automated understanding of our visual world has never been more important. The field is advancing rapidly and its applications continue to expand in areas of great societal value. As computer vision shifts from research to development, there is a critical need for developers with expertise in this field. To meet the growing demand, The Robotics Institute has developed a 16 month (three semesters plus summer) professional Master’s program in Computer Vision (MSCV).

The goals of the MSCV program are to:

  • Provide a robust set of courses encompassing current and emerging state of the art computer vision topics that will prepare students for careers in this field.
  • Facilitate hands-on experience on real research and development projects addressing current applications of computer vision. Students will be assessed via a final project report, coupled with a demonstration and presentation.

The Robotics Institute is home to one of the largest academic groups of computer vision with expertise in relevant sub-ares, including sensing, computational photography, physics-based vision, tracking, 3D reconstruction, statistical analysis, object recognition, human modeling and analysis and general scene understanding. Students enrolled in the MSCV program will have access to world-class computer vision research facilities and a comprehensive list of courses offered by the faculty.

A Growing Field:

    In recent years computer vision has changed the way we view the world. Some examples of computer vision applications include image-based Internet searches, street-view related applications, robotics, face recognition for social networks, safety systems on vehicles, visual product identification and searches, human-computer interfaces for visual communication and gaming, disease diagnostics using medical imaging, visual inspection of machine parts, visual crop quality assessment, etc. To support the rapid development of these applications, major companies including Adobe, Amazon, Apple, Canon, Facebook, GE, Google, IBM, Microsoft, NVIDIA, Qualcomm, Samsung and Siemens as well as numerous start-ups are forming computer vision groups.

MSCV Learning Outcomes:

Upon successful completion of the MSCV program students are expected to be proficient in:

  • Reading and understanding current research publications about state of the art computer vision techniques.
  • Using the fundamental development tools commonly used for developing computer vision applications.
  • Implementing computer vision applications based on state of the art algorithms.
  • Presenting the background and implementation details of a state of the art computer vision technique in a concise and clear manner.
  • Conducting experimental analysis and testing consistent with current practice in computer vision, including standard metrics and benchmark datasets.
  • Applying mathematical and machine learning tools, such as geometry, optimization, and statistics to computer vision applications.

For more information, contact Julie Goldstein (jgolds@cs.cmu.edu; 412-268-4017).

Additional information