MSCV Program Curriculum
The MSCV program is a professional degree that prepares students for industry and a career related to computer vision. It is a full-time 16-month program, spanning three semesters and one summer. Students are required to complete 144 units to be eligible for graduation. The curriculum consists of five core courses (total of 60 units), two MSCV project courses (total of 24 units), two electives (total of 24 units), and a required summer internship (36 units). Since computer vision is a rapidly changing field, this course of study is subject to change in order to reflect state of the art updates.
1st Semester – 36 units (12 units each) | ||
---|---|---|
Course No. | Course Title | |
16-820 | Advanced Computer Vision | |
10-601 | Introduction to Machine Learning | |
16-811 or 16-831 |
Mathematical Fundamentals for Robotics or Statistical Techniques in Robotics |
2nd Semester – 36 units (12 units each) | ||
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Course No. | Course Title | |
16-621 | MSCV Project I | |
xx-xxx | Approved Elective Course | |
15-663 or 16-824 |
Computational Photography or Visual Learning and Recognition |
Summer Term: 36 units of CV-related internship
3rd Semester – 36 units (12 units each) | ||
---|---|---|
Course No. | Course Title | |
16-622 | MSCV Project II | |
xx-xxx | Approved Elective Course | |
16-822 or 16-833 |
Geometry Based Methods in Computer Vision or Robot Localization and Mapping |
The core courses are designed to cover the necessary foundations in math, machine learning, and computer vision. Our goal is to address the two main areas of current computer vision systems (1) recognition (including images and videos, and web-based applications), and (2) geometry (including multi-view reconstruction, Web-scale reconstruction, SLAM). The electives offer a complement of specialized aspects of computer vision, together with further material on machine learning.
Summer internships must be relevant to computer vision. If unsure, students can gain approval from the MSCV Program Director. Students will be registered for 36 units of internship credit and will be required to submit a final report documenting the work that they completed during the internship. The MSCV faculty will review the final report and assign the student a pass/fail grade for his/her work. As an alternative to an internship, students may stay on campus to intern with a Professor. Students are required to complete 36 units of Internship during the summer in order to meet the 144-unit total for graduation.
- MSCV Project I & II – 12 units each (24 units total) – Second and Third Semester
This program will include two project courses to facilitate the development of computer vision software. MSCV Project I & II will be offered in the second and third semesters. The project course will allow students to form small teams that will focus on a hands-on computer vision topic proposed by the course instructor, core faculty, or industry colleagues. Students may propose projects independently but they will have to be reviewed and approved by the core program faculty. All projects will be supervised and coordinated by the MSCV faculty. The project is intended to allow students to acquire hands-on experience and apply concepts and methods taught in class. Students will learn the challenges of real-world software development. The outcome of this course will be a final project report, coupled with a demonstration and presentation.
List of Courses:
Course Title |
Course No. |
Units |
---|---|---|
Electives (choose 2) |
||
Vision Sensors | 16-421 | 12 |
Learning-based Image Synthesis | 16-726 | 12 |
Mechatronic Design | 16-778 | 12 |
Physics-based Methods in Vision | 16-823 | 12 |
Learning for 3D Vision | 16-825 | 12 |
Special Topics: Deep Reinforcement Learning for Robotics | 16-881 | 12 |
Parallel Computer Architecture and Programming | 15-618 | 12 |
Cloud Computing | 15-619 | 12 |
Computer Graphics | 15-662 | 12 |
Physics-based Rendering | 15-668 | 12 |
Graduate Artificial Intelligence | 15-780 | 12 |
Multimedia Databases and Data Mining | 15-826 | 12 |
Special Topics in Theory: Spectral Graph Theory | 15-859N | 12 |
Human Motion Modeling and Analysis | 15-869 | 12 |
Planning, Execution, and Learning | 15-887 | 12 |
Machine Learning with Large Datasets | 10-605 | 12 |
Intermediate Deep Learning | 10-617 | 12 |
Statistical Machine Learning | 10-702 | 12 |
Deep Reinforcement Learning & Control | 10-703 | 12 |
Topics in Deep Learning | 10-707 | 12 |
Probabilistic Graphical Models | 10-708 | 12 |
Deep Learning Systems: Algorithms and Implementation | 10-714 | 12 |
Advanced Machine Learning: Theory and Methods | 10-716 | 12 |
Convex Optimization | 10-725 | 12 |
Machine Learning with Large Datasets | 10-805/11-805 | 12 |
Natural Language Processing | 11-611 | 12 |
Large-Scale Multi-media Analysis | 11-775 | 12 |
Multimodal Affective Computing | 11-776 | 12 |
Advanced Multimodal Machine Learning | 11-777 | 12 |
Intermediate Statistics | 36-705 | 12 |
Advanced Statistical Theory I | 36-755 | 12 |