This page summarizes the course requirements for the standard track, including the core courses, the electives, and the grade requirements.  The requirements for the Computational Mathematics track can be found here


The Core

The set of core courses has been designed to bring sharp focus on the foundations to the program, guarantee sufficient breadth, and foster collegiality among our graduate students. Each student selects a set of five courses from the list below; the selection must include one course in Machine Learning, two courses in Systems, and two courses in Theory.

Core courses: Machine Learning

  • CMSC 35400 - Machine Learning
  • TTIC 31020 - Introduction to Statistical Machine Learning

Core courses: Systems

  • CMSC 33000 - Operating Systems
  • CMSC 33700 - Introduction to Computer Graphics
  • CMSC 32200 - Computer Architecture
  • CMSC 32630 - Advanced Implementation of Computer Languages
  • CMSC 33100 - Advanced Operating Systems
  • CMSC 33300 - Networks & Distributed Systems
  • CMSC 33520 - Data Intensive Systems
  • CMSC 33550 - Introduction to Databases
  • CMSC 33710 - Scientific Visualization

Core courses: Theory

Students choose two out of the following list of courses, including one from Group 1, and one from Group 2.

Group 1

Any of the courses in this group is sufficient as a prerequisite to Algorithms.

  • CMSC 37115 - Introduction to mathematical reasoning via Discrete Mathematics.
  • CMSC 27100 - Discrete Mathematics. *FOOTNOTE At the moment, this undergraduate course is heavily oversubscribed, so we ask PhD students and advisors to consider alternatives.
  • CMSC 27130 - Honors Discrete Mathematics.
  • CMSC 31150 - Mathematical Toolkit.
  • CMSC 38400 - Cryptography.

Group 2

  • CMSC 37000 - Algorithms.
  • CMSC 28100 - Introduction to Complexity Theory.
  • CMSC 38130 - Complexity Theory.
  • CMSC 27500 - Graph Theory.
  • CMSC 37530 - Graph Theory.
  • CMSC 37200 - Combinatorics.

Electives

The list of courses that are available to serve as electives varies significantly year-to-year. In particular, “Topics” courses may or may not be taken as an elective, depending on the offering. Topics courses can be used more than once, provided the material taught in the different offerings is distinct, and the course involves structured and graded work.

The following courses are approved as electives for 2018-2019 (bur see observation about Topic courses above). To ensure sufficient breadth, students must take electives from either (a) two or more areas or (b) from the computational mathematics list.

ELECTIVES

  • CMSC 30100 - Technical Writing and Presentation

Electives: Artificial Intelligence

  • CMSC 45300 - Machine Learning

  • TTIC 31120 - Statistical and Computational Learning Theory

  • TTIC 31040 - Introduction to Computer Vision

  • TTIC 31170 - Planning, Learning, and Estimation for Robotics and Artificial Intelligence

  • TTIC 31210 - Advanced Natural Language Processing

  • TTIC 31220 - Unsupervised Learning and Large-Scale Data Analysis

  • TTIC 31230 - Fundamentals of Deep Learning

  • CMSC 35200 - Deep Learning Systems

  • CMSC 35401 - Topics in Machine Learning

  • CMSC 35425 - Topics in Statistical Machine Learning

  • CMSC 35050 - Computational Linguistics

  • CMSC 33350 Neuromorphic Computing

  • TTIC 31230 Fundamentals of Deep Learning

  • STAT 37710 Topics in Statistical Machine Learning

Electives: Computational Mathematics

  • CMSC 30900 - Mathematical Computation I: Matrix Computation

Electives: Systems

  • Any course from the systems core course list that is not used by the student to fulfill the core requirements.

  • CMSC 33250 - Introduction to Computer Security

  • CMSC 33520 - Data Intensive Computing Systems

  • CMSC 33301- Topics in Systems (several offerings)

  • CMSC 32201 - Topics in Computer Architecture

  • CMSC 33200 - Topics in Operating Systems

  • CMSC 33250 - Topics in Computer Security

  • CMSC 34702 Topics in Networks

  • CMSC 32001 - Topics in Programming Languages

  • CMSC 33251 Topics in Security

  • CMSC 35200 - Deep Learning Systems

  • CMSC 33210 - Usable Security and Privacy

  • CMSC 33400 Mobile Computing

Electives: Theory

  • Any course from the theory core course list that is not used by the student to fulfill the core requirements.

  • CMSC 39010 - Computational and Metric Geometry

  • CMSC 39600 - Topics in Theoretical Computer Science

  • CMSC 38815 - Geometric Complexity

  • CMSC 38130 Complexity Theory

  • CMSC 38502 Topics in Combinatorics and Logic

  • CMSC 39000 Computational Geometry

  • MATH 38800 Complexity Theory

Students may petition the Graduate Committee to substitute other courses for those listed. Students are required to submit their petitions for substitution before they take a course with which they intend to fulfill the electives requirement.


Grade Requirements

There are specific grade requirements for both core courses and electives (described below). The spirit of these requirements can be summed up by the following motto: a student must demonstrate proficiency in all areas and excellence in at least one area.

The minimum formal requirements for the core courses ("Ph.D. Pass") are the following: Students are required to complete the five core courses with a grade point average (GPA) of at least 3.25 in the five core courses. In computing the GPA, A=4, B=3, and a + or a - counts as .3 of a point. Note that for the core courses, students who significantly outperform even the typical "A" students may receive a grade of "A+" (recorded internally by the CS Department Student Representative--the University does not officially grant the grade of A+.) So, for instance a student with grades A+, B+, B+, B-, B- in the five core courses has a GPA of 3.26 and thus satisfies the minimum GPA requirement, as does a student with grades of A+, A, B+, B, and C-. In the graduate program grades below C- are not passing grades.

Students must complete their electives with a grade of B or better in each course.

The previous rules, that apply to the classes before the cohort first enrolled in Summer or Autumn 2015 can be found here.

Students who fail to meet the core course requirements stated in the preceding paragraph may continue on to write a master's paper and complete a master's degree, if they meet the following requirement ("Master's Pass"): complete all the five core courses by the end of the spring quarter of the second year with a grade of at least C- in each core course and with a grade point average (GPA) of at least 3.00 in the five core courses. Such students will be supported for at most one quarter of their third year.

Students who do not meet the Ph.D. Pass requirements for these courses cannot continue their studies beyond autumn quarter of their third year. Students who do meet these minimum requirements will not automatically be allowed to continue after their third year; the faculty will decide continuation based on the student's perceived capacity to perform Ph. D. level independent research in a specific area.