Master of Science in Computer Science
The program for the Master of Science in Computer Science (MSCS) prepares students for more highly productive careers in industry. Graduates receive the MSCS for completing one of three options in the program as described in this section. Students may apply to the program if they possess a bachelor's degree in computer science from an accredited institution. Students without a bachelor's degree in computer science are encouraged to apply as well, with the understanding that they will be required to complete remedial coursework appropriate to their background in addition to the requirements of the MSCS degree. All applicants are evaluated according to their prior academic record, scores on the Graduate Record Examination, a personal statement, and letters of recommendation. Applicants are selected for fall semester admission only. The application deadline is February 1. However, all applicants are encouraged to apply as early as possible because the selection process may begin well before the deadline.
Students entering the program must demonstrate a core competency in computing equivalent to undergraduate-level courses in the following areas:
- systems, design and analysis of algorithms,
- formal languages and automata theory,
- databases,
- networking and communications,
- computer architecture, and
- human-computer interaction.
This requirement can be satisfied by having taken undergraduate courses as a part of an undergraduate degree, taking remedial courses in the MS CS program, or by examination. Students may specialize in areas of their choice. Every student must complete at least one specialization as a part of their degree program. The current eleven specialization areas are:
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Computer Graphics, Computing Systems
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High-Performance Computing, Human-Computer Interaction
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Interactive Intelligence
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Machine Learning
-
Modeling and Simulations
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Scientific Computing
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Social Computing
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Visual Analytics.
A student who is enrolled in another graduate program of the Institute may pursue an MSCS while that student is also pursuing their degree in the other major. To be granted permission to pursue the MSCS, a student must submit to the MS program coordinator of the College of Computing the material required for admission to the MSCS program. This includes transcripts, letters of recommendation, and GRE General Test. If the student is approved by the College to pursue the MSCS, the student will be notified in writing. At no time will a student outside the College be allowed to pursue a concurrent degree without prior permission of the MS program coordinator of the College of Computing.
A student enrolled in the MS degree program in computer science who wishes to be admitted to the PhD program in computer science should apply via the same process as external students. It is expected that such a student will have at least two letters of recommendation from College of Computing faculty.
For more information about the MS CS program, visit www.cc.gatech.edu.
Program of Study
The College's master's degree requirements supplement the Institute's master's requirements listed in this catalog:
- Students must achieve a grade-point average of at least 3.0 to graduate, and no course grades below C will count toward graduation. Students must take all master's degree coursework on a letter-grade basis.
- Undergraduate courses required for the BSCS degree may not be used toward the MSCS degree. In addition, no graduate credit will be given for 3000 level courses or lower-level courses.
- No course may be used to satisfy the requirements of two degrees. In addition, no graduate credit will be given for CS courses with a number lower than 4140.
- A maximum of 6 hours may be taken at the 4000-level and/or with a subject code other than CS or CSE. See exceptions under the Project and Thesis options below.
- The maximum total credit hours of Special Problems (CS or CSE 89xx) that may be applied toward the MSCS degree is 3. These courses must be within the CoC.
- There is no maximum number of Special Topics (CS or CSE 88xx) courses that may be used towards the degree.
- Each student must complete the requirements for one specialization. Click here for specialization requirements.
- Students may choose from one of three options in pursuing the MSCS degree, including:
Course Option
This option requires the student to complete 30 hours of coursework.
| Code | Title | Credit Hours |
|---|---|---|
| Total Course Credit Hours (no MS project or thesis hours) | 30 | |
| CS and CSE (minimum 24 credit hours) | 24 | |
| CS and CSE 6000-8000 Level Courses (minimum 24 credit hours) | 24 | |
| 6000/8000 Level Courses (minimum 24 credit hours) | 24 | |
Project Option
This option requires the student to complete 21 credit hours of coursework and a 9 credit hour project. The project requires approval by a faculty advisor and the MS program coordinator in the semester prior to its inception.
| Code | Title | Credit Hours |
|---|---|---|
| Total Coursework Credit Hours | 21 | |
| MSCS Project hours (CS 6999) | 9 | |
| Total Credit Hours | 30 | |
| CS and CSE Courses (minimum of 15 credit hours) 1 | 15 | |
| CS and CSE 6000-8000 Level Courses (minimum of 15 credit hours) 1 | 15 | |
- 1
May not include MS project or thesis hours.
Thesis Option
This option requires the student to complete 18 credit hours of coursework and a 12 credit hour thesis. The thesis process is defined elsewhere in this catalog.
| Code | Title | Credit Hours |
|---|---|---|
| Total Coursework Credit Hours | 18 | |
| MS Thesis Credit Hours 2 | 12 | |
| Total Credit Hours | 30 | |
| CS and CSE Courses (minimum of 15 credit hours) 1 | 15 | |
| CS and CSE 6000-8000 Level Courses (minimum of 15 credit hours) 1 | 15 | |
- 1
May not include MS project or thesis hours.
- 2
The student must obtain advance approval of the thesis proposal by the faculty advisor and MSCS coordinator. See your academic advisor for more information about the thesis process.
Specializations
Computational Perception and Robotics
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Algorithms: Pick one (1) of: | 3 | |
| Computability, Algorithms, and Complexity | ||
| Introduction to Graduate Algorithms | ||
| Computational Complexity Theory | ||
| Design and Analysis of Algorithms | ||
| Approximation Algorithms | ||
| Randomized Algorithms | ||
| Computational Science and Engineering Algorithms | ||
| And, pick one (1) of: | 3 | |
| Artificial Intelligence | ||
| Machine Learning | ||
| Electives | ||
| Pick three (3) courses from Perception and Robotics, with at least one from each: | 9 | |
| Perception | ||
| Computational Photography | ||
| Introduction to Computer Vision GR | ||
| 3D Reconstruction and Mapping in Computer Vision, Robotics, and Augmented Reality | ||
| Computational Perception | ||
| Cyber Physical Design and Analysis | ||
| Machine Learning for Robotics | ||
| Natural Language | ||
| Robotics | ||
| Autonomous Robotics | ||
| Autonomous Multi-Robot Systems | ||
| Human-Robot Interaction | ||
| Artificial Intelligence Techniques for Robotics | ||
| Interactive Robot Learning | ||
| Robot Intelligence: Planning | ||
| Total Credit Hours | 15 | |
Computer Graphics
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Pick one of: | 3 | |
| Foundations of Computer Graphics | ||
| Video Game Design and Programming | ||
| Computer Animation | ||
| Pick one of: | 3 | |
| Computability, Algorithms, and Complexity | ||
| Introduction to Graduate Algorithms | ||
| Electives: select three (3) of: | 9 | |
| Video Game Design and Programming | ||
| Computational Photography | ||
| Introduction to Computer Vision GR | ||
| Foundations of Computer Graphics | ||
| Shape Grammars | ||
| Data Visualization: Principles and Applications | ||
| Computer Animation | ||
| Total Credit Hours | 15 | |
Computing Systems
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| CS 6505 | Computability, Algorithms, and Complexity | 3 |
| or CS 6515 | Introduction to Graduate Algorithms | |
| And, pick two (2) of: | 6 | |
| Advanced Operating Systems | ||
| Design and Implementation of Compilers | ||
| Computer Networks | ||
| High-Performance Computer Architecture | ||
| Software Development Process | ||
| Programming Language Design | ||
| Database Systems Concepts and Design | ||
| Electives: pick three (3) of: 1,2 | 9 | |
| Introduction to Information Security | ||
| Graduate Introduction to Operating Systems | ||
| Big Data Systems and Analytics | ||
| Real-Time System Concepts and Implementation | ||
| Secure Computer Systems | ||
| Applied Cryptography | ||
| Network Security | ||
| Intro to Cyber-Physical Systems Security | ||
| Embedded Software Optimizations | ||
| Software Architecture and Design | ||
| Advanced Topics in Software Analysis and Testing | ||
| Intro Enterprise Comput. | ||
| Database System Implementation | ||
| Design and Analysis of Algorithms | ||
| Advanced Internet Computing Systems and Applications | ||
| Distributed Computing | ||
| Internetworking Architectures and Protocols | ||
| Networked Applications and Services | ||
| Network Science: Methods and Applications | ||
| Advanced Topics in Microarchitecture and organization of high-performance processors. | ||
| Reliability and Security in Computer Architecture | ||
| Theoretical Foundations of Cryptography | ||
| Special Topics (Foundations of Programming Languages) | ||
| High Performance Computing | ||
| Total Credit Hours | 18 | |
- 1
Any Core Courses in excess of the 9 hour requirement may be used as Computing Systems Electives
- 2
Any Special Topics (CS 8803) course that is being taught by a School of Computer Science faculty member may also count as a Computing Systems elective. The definition of “School of Computer Science faculty member” is a faculty member who appears on the School of Computer Science website https://scs.gatech.edu/people/faculty.
High-Performance Computing
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| CSE 6140 | Computational Science and Engineering Algorithms | 3 |
| CSE 6220 | High Performance Computing | 3 |
| Electives | ||
| Pick three (3) of: | 9 | |
| Multicore Computing: Concurrency and Parallelism on the Desktop | ||
| High-Performance Parallel Computing: Tools and Applications | ||
or CSE 6230 | High Performance Parallel Computing: Tools and Applications | |
| Design and Implementation of Compilers | ||
| High-Performance Computer Architecture | ||
| Special Topics (Parallel Numerical Algorithms) | ||
or CSE 8803 | Special Topics | |
| Parallel and Distributed Simulation | ||
| Special Topics (Hot Topics in Parallel Computing) | ||
| Total Credit Hours | 15 | |
Human-Centered Computing
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| CS 6451 | Introduction to Human-Centered Computing | 3 |
| CS 6452 | Prototyping Interactive Systems | 3 |
| CS 7455 | Issues in Human-Centered Computing | 3 |
| Electives | ||
| Pick two (2) of: | 6 | |
| User Interface Design and Evaluation | ||
| Principles of User Interface Software | ||
| Educational Technology: Conceptual Foundations | ||
| Computational Journalism | ||
| Design of Online Communities | ||
| Computational Social Science | ||
| Social Computing | ||
| Introduction to Computer Vision GR | ||
| Artificial Intelligence | ||
| Data Visualization: Principles and Applications | ||
| Human-Computer Interaction | ||
| Introduction to Cognitive Science | ||
| Information Visualization | ||
| Human-Centered Data Analysis | ||
| Collaborative Computing | ||
| Machine Learning | ||
| Mobile and Ubiquitous Computing | ||
| Advanced Computer Vision | ||
| Modeling and Design | ||
| Game Artificial Intelligence | ||
| Human-Robot Interaction | ||
| Knowledge-Based AI | ||
| Case-Based Reasoning | ||
| Natural Language | ||
| Philosophy of Cognition | ||
| Cognitive Models of Science and Technology | ||
| Cognitive Modeling | ||
| Special Topics (Computational Creativity) | ||
| Special Topics (Expressive AI) | ||
| Special Topics (Computers, Communications & International Development) | ||
| Total Credit Hours | 15 | |
Human-Computer Interaction
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| CS 6456 | Principles of User Interface Software | 3 |
| or CS 7470 | Mobile and Ubiquitous Computing | |
| CS 6750 | Human-Computer Interaction | 3 |
| Electives | ||
| Pick three (3) from the two sub-areas below, including one from each sub-area: | 9 | |
| Sub-area: Design and evaluation concepts | ||
| Principles of Design | ||
| Software Requirements Analysis and Specification | ||
| Digital Health Equity | ||
| User Interface Design and Evaluation | ||
| Video Game Design and Programming | ||
| Educational Technology: Conceptual Foundations | ||
| Computational Journalism | ||
| Design of Online Communities | ||
| Introduction to Cognitive Science | ||
| Educational Technology: Design and Evaluation | ||
| Computer-Supported Collaborative Learning | ||
| Cognitive Modeling | ||
| Sub-area: Interactive technology | ||
| Information to Health Informatics | ||
| Data Visualization: Principles and Applications | ||
| Design of Design Environments | ||
| Mixed Reality Experience Design | ||
| Information Visualization | ||
| Human-Centered Data Analysis | ||
| Collaborative Computing | ||
| Mobile and Ubiquitous Computing | ||
| Game Artificial Intelligence | ||
| Total Credit Hours | 15 | |
Interactive Intelligence
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Take one (1) of: | 3 | |
| Software Development Process | ||
| Advanced Topics in Software Engineering | ||
| Computability, Algorithms, and Complexity | ||
| Introduction to Graduate Algorithms | ||
| Computational Science and Engineering Algorithms | ||
| Take two (2) of: | 6 | |
| Artificial Intelligence | ||
| Knowledge-Based AI | ||
| Machine Learning | ||
| Electives | ||
| Pick two (2) courses from: | 6 | |
| Interaction | ||
| Information to Health Informatics | ||
| Educational Technology: Conceptual Foundations | ||
| Computational Journalism | ||
| Computational Social Science | ||
| AI, Ethics, and Society | ||
| Human-Computer Interaction | ||
| AI Methods | ||
| Introduction to Computer Vision GR | ||
| Autonomous Multi-Robot Systems | ||
| Game Artificial Intelligence | ||
| Human-Robot Interaction | ||
| AI Storytelling in Virtual Worlds | ||
| Deep Learning | ||
| Machine Learning with Limited Supervision | ||
| Natural Language | ||
| Special Topics (Advanced Game AI ) | ||
| Cognition | ||
| Introduction to Cognitive Science | ||
| Modeling and Design | ||
| Human and Machine Learning | ||
| Special Topics (Computational Creativity) | ||
| Total Credit Hours | 15 | |
Machine Learning
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Algorithms | ||
| Pick one (1) of: | 3 | |
| Computability, Algorithms, and Complexity | ||
| Introduction to Graduate Algorithms | ||
| Computational Complexity Theory | ||
| Design and Analysis of Algorithms | ||
| Graph Algorithms | ||
| Approximation Algorithms | ||
| Randomized Algorithms | ||
| Computational Science and Engineering Algorithms | ||
| Pick one (1) of: | 3 | |
| Machine Learning | ||
| Computational Data Analysis: Learning, Mining, and Computation | ||
| Electives | ||
| Pick three (3) of: | 9 | |
| Big Data Systems and Analytics | ||
| Introduction to Computer Vision GR | ||
| AI, Ethics, and Society | ||
| Network Science: Methods and Applications | ||
| Markov Chain Monte Carlo Algorithms | ||
| Spectral Algorithms and Representations | ||
| Theoretical Foundations of Machine Learning | ||
| Pattern Recognition | ||
| Introduction to Behavioral Imaging | ||
| Reinforcement Learning and Decision Making | ||
| Deep Learning | ||
| Machine Learning for Robotics | ||
| Machine Learning for Trading | ||
| Natural Language | ||
| Special Topics (Probabilistic Graph Models) | ||
| Web Search and Text Mining | ||
| Data and Visual Analytics | ||
| Big Data Analytics for Healthcare | ||
| Computational Statistics | ||
| Introduction to Theory and Practice of Bayesian Statistics | ||
| Stochastic Optimization | ||
| Total Credit Hours | 15 | |
Modeling and Simulations
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| CSE 6730 | Modeling and Simulation: Foundations and Implementation | 3 |
| Pick one (1) of: | 3 | |
| High Performance Computing | ||
| Simulation | ||
| Introduction to Numerical Methods for Partial Differential Equations | ||
| Electives | ||
| Pick three (3) of: | 9 | |
| High Performance Computing | ||
| Parallel and Distributed Simulation | ||
| Special Topics (Quantum Information, Computation, and Simulation) | ||
| Network Science: Methods and Applications | ||
| Modeling, Simulation and Military Gaming | ||
| Simulation | ||
| Introduction to Numerical Methods for Partial Differential Equations | ||
| Total Credit Hours | 15 | |
Scientific Computing
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| CSE/MATH 6643 | Numerical Linear Algebra | 3 |
| Pick one (1) of: | 3 | |
| Iterative Methods for Systems of Equations | ||
| Introduction to Numerical Methods for Partial Differential Equations | ||
| Electives | ||
| Pick three (3) of: | 9 | |
| High-Performance Parallel Computing: Tools and Applications | ||
| Special Topics (Parallel Numerical Algorithms) | ||
| Computational Science and Engineering Algorithms | ||
| High Performance Computing | ||
| Iterative Methods for Systems of Equations | ||
| Special Topics (Algorithms for Medical Imaging and Inverse Problems) | ||
| Special Topics (Computational Chemistry) | ||
| Introduction to Numerical Methods for Partial Differential Equations | ||
| Total Credit Hours | 15 | |
Social Computing
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| Pick two (2) of: | 6 | |
| Design of Online Communities | ||
| Social Computing | ||
| Computational Social Science | ||
| Electives | ||
| Pick three (3) classes including additional classes from above and: | 9 | |
| Secure Computer Systems | ||
| Computer Networks | ||
| Principles of User Interface Software | ||
| Computational Journalism | ||
| Computability, Algorithms, and Complexity | ||
| Introduction to Graduate Algorithms | ||
| Advanced Internet Computing Systems and Applications | ||
| Data Visualization: Principles and Applications | ||
| Human-Computer Interaction | ||
| Distributed Computing | ||
| Networked Applications and Services | ||
| Network Science: Methods and Applications | ||
| Information Visualization | ||
| Human-Centered Data Analysis | ||
| Computer-Supported Collaborative Learning | ||
| Natural Language | ||
| Total Credit Hours | 15 | |
Visual Analytics
| Code | Title | Credit Hours |
|---|---|---|
| Core Courses | ||
| CSE 6242 | Data and Visual Analytics | 3 |
| CS 6730 | Data Visualization: Principles and Applications | 3 |
| CS 7450 | Information Visualization | 3 |
| Electives | ||
| Pick two (2) from: | 6 | |
| Principles of User Interface Software | ||
| Computational Journalism | ||
| Foundations of Computer Graphics | ||
| Human-Computer Interaction | ||
| Introduction to Cognitive Science | ||
| Human-Centered Data Analysis | ||
| Machine Learning | ||
| Computational Data Analysis: Learning, Mining, and Computation | ||
| Total Credit Hours | 15 | |
The Master of Science in Computer Science is also offered online.
For more information, visit: Online Master of Science in Computer Science.