everything.study
COMP SCI 771

LEARNING BASED METHODS FOR COMPUTER VISION

3 credits.

Addresses the problems of representation and reasoning for large amounts of visual data, including images and videos, medical imaging data, and their associated tags or text descriptions. Introduces deep learning in the context of computer vision. Covers topics on visual recognition using deep models, such as image classification, object detection, human pose estimation, action recognition, 3D understanding, and medical image analysis. Emphasizes the design of vision and learning algorithms and models, as well as their practical implementations. Strongly recommended to have knowledge in computer vision or machine learning [such as COMP SCI 540 ] or medical image analysis [such as B M I / COMP SCI/B M I 567 ].

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Demonstrate their understanding of basic theories, current approaches, key concepts, and common practices in the area of deep learning for computer vision. Audience: Graduate 2. Recognize and distinguish among a variety of visual recognition problems in computer vision, including their problem formulations and evaluation metrics. Audience: Graduate 3. Utilize and implement deep learning models to solve visual recognition problems. Audience: Graduate 4. Design deep learning models for visual recognition problems, conduct experiments to evaluate the proposed model, and analyze and interpret the results. Audience: Graduate 5. Communicate effectively through written reports, oral presentations, and discussions. Audience: Graduate Learning Outcomes: 1. Demonstrate their understanding of basic theories, current approaches, key concepts, and common practices in the area of deep learning for computer vision. Audience: Graduate 2. Recognize and distinguish among a variety of visual recognition problems in computer vision, including their problem formulations and evaluation metrics. Audience: Graduate 3. Utilize and implement deep learning models to solve visual recognition problems. Audience: Graduate 4. Design deep learning models for visual recognition problems, conduct experiments to evaluate the proposed model, and analyze and interpret the results. Audience: Graduate 5. Communicate effectively through written reports, oral presentations, and discussions. Audience: Graduate

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