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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 approval by 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 hosts guest speakers from academia who will present their current work. Potential industry sponsors will also visit to present potential projects to the students. By the end of the semester, MSCV 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, and will learn a great deal about being a successful member of a team. 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

    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)

    Advanced 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: 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

    Planning, Execution, and Learning

    15-887

    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 Computation

    11-776

    12

    Advanced Multimodal Machine Learning

    11-777

    12

    Machine Learning with Large Datasets

    11-805/10-805

    12

    Intermediate Statistics

    36-705

    12

    Advanced Statistical Theory I

    36-755

    12