MSCV Program Curriculum 2017-04-14T10:27:07+00:00

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), a seminar course (0 units, 2 electives (total of 24 units) and a required summer Practicum or Internship (36 units).

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 one seminar course and 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 Seminar – 0 units – First Semester

The MSCV Seminar course will be offered during the first semester. The program host guest speakers from industry and academia who will present their current work and potential projects to the students. By the end of the semester, the students should have a clear idea of what project they want to work on for MSCV Project I and II (see below).

  • 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 and Seminar Courses

MSCV Seminar 16-627 0
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
Physics-based Methods in Vision 16-823 12
Statistical Techniques in Robotics 16-831 12
Robot Localization and Mapping 16-833 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
Parallel Computer Architecture and Programming 15-640 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
Statistical Machine Learning 10-702 12
Deep Reinforcement Learning & Control 10-703 12
Probabilistic Graphical Models 10-708 12
Convex Optimization 10-725 12
Large-Scale Multi-media Analysis 11-775 12
Human Communication and Multimodal Machine Learning 11-776 12
Advanced Multimodal Machine Learning 11-777 12
Machine Learning with Large Datasets 11-805/10-805 12
Topics in Deep Learning 10-807 12
Intermediate Statistics 36-705 12
Advanced Statistical Theory I 36-755 12