MSCV Program Curriculum

The MSCV program is a full-time 16 month (three semesters plus summer) program. Students are required to complete 144 units to be eligible for graduation. The curriculum consists of 5 core courses (total of 60 units), 2 MSCV project courses (total of 24 units), 2 electives (total of 24 units), and a required summer Practicum or Internship (36 units).

1st Semester, Fall Term – 36 units (12 units each)
Course No. Course Title
16-720 B Computer Vision
16-811 Mathematical Fundamentals for Robotics
10-601 Introduction to Machine Learning
2nd Semester, Spring Term – 36 units (12 units each)
Course No. Course Title
16-824 Visual Learning and Recognition
16-621 MSCV Project I
xx-xxx Approved Elective Course

 Summer Term: 36 units of CV-related internship

3rd Semester, Fall Term – 36 units (12 units each)
Course No. Course Title
16-822 Geometry Based Methods in Computer Vision
16-622 MSCV Project II
xx-xxx Approved Elective Course

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. This program will include two project courses to facilitate the development of computer vision software. Summer internships must be relevant to computer vision and require pre-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 complete Practicum with a Professor. Students are required to complete 36 units of Internship or Practicum 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

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.


Core Courses

Computer Vision 16-720 12
Introduction to Machine Learning 10-601 12
Mathematical Fundamentals for Robotics 16-811 12
Visual Learning and Recognition 16-824 12
Geometry-based Methods in Vision 16-822 12

Project Courses

MSCV Project I 16-621 12
MSCV Project II 16-622 12

Electives (choose 2)

Vision Sensors 16-421 12
Designing Computer Vision Apps 16-623 12
Mechatronic Design 16-778 12
Physics-based Methods in Vision 16-823 12
Statistical Techniques in Robotics 16-831 12
Robot Localization and Mapping 16-833 12
Special Topics: Deep Reinforcement Learning for Robotics 16-881 12
Special Topics: The Visual World as seen by Neurons and Machines 16-899A 12
Special Topics: Big Data Approaches in Computer Vision 16-899D 12
Special Topics: Human Analysis 16-899H 12
Parallel Computer Architecture and Programming 15-618 12
Cloud Computing 15-619 12
Computer Graphics 15-662 12
Computational Photography 15-663 12
Artificial Intelligence: Representation and Problem Solving 15-781B 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
Special Topics in Signal Processing: Compressive Sensing and Sparse Optimization 18-799J 12
Machine Learning with Large Datasets 10-605 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
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