In order to complete the Master of Science in Intelligent Information Systems, a student must satisfy three types of requirements. Curricular requirements ensure that MIIS students receive instruction in core intelligent information systems technologies while also allowing an opportunity to specialize in areas of personal interest. Practice requirements are opportunities to apply and hone new skills while building state-of-the-art systems. Grade requirements ensure that students have demonstrated a certain level of skill while completing degree requirements. All three types of requirements are described below.

Curricular Requirements

A student must complete at least 84 instruction-oriented course units and satisfy the following curricular requirements.

  1. Qualifying courses: Students must pass 72 units (typically 6 12-unit courses) in qualifying Masters courses. A qualifying Masters course is defined as:
    • Any graduate course (600-level or higher) offered by the Language Technologies Institute; and
    • Any graduate course (600-level or higher) from a list of approved qualifying courses.
  2. Free elective: Students must pass 12 units (typically 1 course) in elective Masters course(s). A free elective is defined as:
    • Any graduate course (600-level or higher) offered by the university; and
    • Any course approved by the student's advisor and the degree Program Director.
    • A student may not use the same course to satisfy both a qualifying course requirement and an elective course requirement.
  3. Breadth requirements: Students must demonstrate breadth by passing a course in each of the following areas.

The department maintains and publishes a list of courses that satisfy each of these requirements [1, 2, 3]. Courses used to satisfy a breadth requirement can also satisfy qualifying course requirements.

Practice Requirements

A student must complete at least 66 practice-oriented course units and satisfy the following practice-oriented requirements.

  1. Directed study requirement: Students must pass 24 units (typically 12 units x 2 semesters) in directed study under the supervision of their advisor. Directed study is a structured, task-oriented form of independent study that provides deep, hands-on experience in a particular technology area and an opportunity to work closely with a member of the faculty.
  2. Internship requirement: Students must complete a one-semester (typically summer) internship at an organization (typically a company or government agency) approved by the MIIS Program Director. Internships are an opportunity to apply new skills in a professional setting and to learn about software development in a 'real world' organization. Students with prior professional experience may petition the MIIS Program Director to waive this requirement.
  3. Capstone requirements: Students must complete a capstone project (36 units) and a capstone planning seminar (6 units). The capstone requirement gives students experience with collaborative, team-oriented software development; significant hands-on experience with the techniques studied in the classroom; and an opportunity to work on a large software application.
    • The capstone project (36 units) is a large, group-oriented demonstration of student skill in one or more areas covered by the degree. Typically the result of the capstone project is a major software application. The capstone project is supervised by a member of the faculty who meets with students on a weekly basis to monitor progress and provide guidance.
    • The capstone planning seminar (6 units) organizes students into groups; defines capstone project goals, requirements, success metrics, and deliverables; and identifies and acquires data, software, and other resources required for successful completion of the project. The planning seminar must be completed in the semester prior to taking the capstone project.

Grade Requirements

Students must demonstrate their mastery of material taught in courses and their success in applying their skills in directed study and capstone projects by satisfying the following grade requirements:

  1. Minimum grade: Only courses, directed study, and projects with a grade of C or higher are counted as satisfying a degree requirement.
  2. Minimum QPA: A student must maintain an average QPA of at least 3.0 in courses, directed study, and projects used to satisfy degree requirements.
  3. Pass/fail: Pass/fail grades are not permitted for courses and projects used to satisfy a degree requirement. Graduate students who are required to take additional undergraduate courses to build up the core foundations of computer science may not elect the pass/fail option for these courses.

Approved Qualifying Courses

Any graduate course (600-level or higher) offered by the Language Technologies Institute is a qualifying course. In addition, any course from the following list is an approved qualifying course.

  • 02-712 Computational Methods for Biological Modeling and Simulation
  • 05-631 Software Structures for User Interfaces
  • 05-813 Human Factors
  • 08-731 Information Security and Privacy
  • 08-766 Mobile and Pervasive Computing Services
  • 10-601 Machine Learning
  • 10-605 Machine Learning with Large Datasets
  • 10-708 Probabilistic Graphical Models
  • 11-492 Speech Processing
  • 16-720 Computer Vision
  • 15-826 Multimedia Databases and Datamining

Breadth Courses: Human Language

  • 11-611 Natural Language Processing
  • 11-711 Algorithms for NLP
  • 11-721 Grammars and Lexicons
  • 11-761 Language and Statistics

Breadth Courses: Language Technology Applications

  • 11-642 Search Engines
  • 11-717 Language Technologies for Computer Assisted Language Learning
  • 11-718 Conversational Interfaces
  • 11-731 Machine Translation
  • 11-751 Speech Recognition and Understanding
  • 11-773 Text-Driven Forecasting
  • 11-797 Question Answering

Breadth Courses: Machine Learning

  • 11-641 Machine Learning for Text Mining
  • 11-663 Machine Learning in Practice
  • 11-755 Machine Learning for Signal Processing
  • 11-763 Structured Prediction for Language and Other Discrete Data
  • 10-601 Machine Learning
  • 10-605 Machine Learning with Large Datasets
  • 10-708 Probabilistic Graphical Models

Immigration Course (IC)

Each Fall semester the LTI provides 2-3 weeks of lectures and talks to help students learn about the work done by CMU faculty and to provide an opportunity for advisors to recruit new students. These talks are known as the "immigration course (IC)" because students are expected to attend them and treat them as seriously as a course; however, they are not actually a course; students do not register for the immigration course, nor do they receive a grade.