Program Overview

Introduction to the Master of Science in Computer Vision (MSCV) Program

The Master of Science in Computer Vision (MSCV) program is a professional degree designed to prepare students for a career in industry related to computer vision. This full-time program spans 16 months, covering three semesters and one summer, and requires students to complete 111 units to be eligible for graduation.

Program Curriculum

The MSCV program curriculum consists of four core courses, three core options, and three electives. The core courses are designed to cover the necessary foundations in math, machine learning, and computer vision, addressing the two main areas of current computer vision systems: recognition and geometry.

Core Courses

• Semester #1: 16-820 Advanced Computer Vision
• Semester #2: 16-621 MSCV Capstone
• Summer: 16-991 B Internship (3 units)
• Semester #3: 16-622 MSCV Capstone

Core Options

• 10-601 Intro to ML OR 16-831 Intro to Robot Learning
• 15-663 Computational Photography OR 16-824 Visual Learning & Recognition
• 16-822 Geometry-based Methods in CV OR 16-833 Robot Localization & Mapping

Electives

Based on availability, students choose three electives from a pre-approved list, which includes but is not limited to:

• Vision Sensors (16-421, 12 units)
• F1Tenth Autonomous Racing (16-663, 12 units)
• Methods in (Bio)Medical Image Analysis (16-725, 12 units)
• Learning-based Image Synthesis (16-726, 12 units)
• Mechatronic Design (16-778, 12 units)
• Mathematical Fundamentals for Robotics (16-811, 12 units)
• Geometry-based Methods in Vision (16-822, 12 units)
• Physics-based Methods in Vision (16-823, 12 units)
• Visual Learning and Recognition (16-824, 12 units)
• Learning for 3D Vision (16-825, 12 units)
• Introduction to Robot Learning (16-831, 12 units)
• Robot Localization and Mapping (16-833, 12 units)
• Special Topics: Deep Reinforcement Learning for Robotics (16-881, 12 units)
• Understanding and Critiquing Generative Computer Vision (16-895, 12 units)
• Parallel Computer Architecture and Programming (15-618, 12 units)
• Cloud Computing (15-619, 12 units)
• Computer Graphics (15-662, 12 units)
• Computational Photography (15-663, 12 units)
• Physics-based Rendering (15-668, 12 units)
• Graduate Artificial Intelligence (15-780, 12 units)
• Multimedia Databases and Data Mining (15-826, 12 units)
• Special Topics in Theory: Spectral Graph Theory (15-859N, 12 units)
• Human Motion Modeling and Analysis (15-869, 12 units)
• Planning, Execution, and Learning (15-887, 12 units)
• Introduction to Machine Learning (10-601, 12 units)
• Machine Learning with Large Datasets (10-605, 12 units)
• Intermediate Deep Learning (10-617, 12 units)
• Statistical Machine Learning (10-702, 12 units)
• Deep Reinforcement Learning & Control (10-703, 12 units)
• Topics in Deep Learning (10-707, 12 units)
• Probabilistic Graphical Models (10-708, 12 units)
• Deep Learning Systems: Algorithms and Implementation (10-714, 12 units)
• Advanced Machine Learning: Theory and Methods (10-716, 12 units)
• Optimization for Machine Learning (10-725, 12 units)
• Machine Learning with Large Datasets (10-805/11-805, 12 units)
• Natural Language Processing (11-611, 12 units)
• Large Language Models: Methods & Applications (11-667, 12 units)
• Large-Scale Multi-media Analysis (11-775, 12 units)
• Multimodal Affective Computing (11-776, 12 units)
• Advanced Multimodal Machine Learning (11-777, 12 units)
• Autonomous Driving (18-744, 12 units)
• Intermediate Statistics (36-705, 12 units)
• Advanced Statistical Theory I (36-755, 12 units)

MSCV Project I & II

The program includes two project courses, MSCV Project I & II, offered in the second and third semesters. These projects allow students to form small teams focusing on hands-on computer vision topics proposed by the course instructor, core faculty, or industry colleagues. The outcome of this course is a final project report, coupled with a demonstration and presentation.

Internship Requirement

Students are required to complete 3 units of internship during the summer, which must be relevant to computer vision. If unsure, students can gain approval from the MSCV Program Director. Students must register for 3 units of internship credit and submit a final report documenting their work, which will be reviewed by the MSCV faculty for a pass/fail grade. Alternatively, students may stay on campus to intern with a professor.