Master of Science (M.Sc.) Computer Science (Non-thesis) – Information

McGill’s Master of Science (M.Sc.) Computer Science (Non-thesis) aims to prepare its students for high-end industry positions involving advanced development.

Students will learn about the latest developments in research and cutting edge technology in the classroom through advanced computer science courses given by the School’s research professors. There is the possibility to apply the knowledge and gain hand-on experience through an academic research project or a 4-month industrial internship. As such this program equips students with both the fundamental background as well as the technical skills that are needed to contribute to a rapidly evolving field.

In many cases, it will be possible to complete this 45-credit program in 16 full-time months (typically Fall/Winter/Summer/Fall).

The detailed program description can be found here.

Students will attend talks throughout the first year in the School’s Computer Science Seminar to get a broad insight of current research challenges (1-credit COMP 602 in Fall and 1-credit COMP 603 in Winter). Furthermore, they take at least 7-8 complementary computer science courses (28 credits). Students can take some of these complementary courses outside the School of Computer Science (e.g., in another university or in another department at McGill) with approval of the academic advisor.

For the remaining credits, they have three options .
  • Firstly, they can conduct a moderate-scale 4-month research project (a total of 15 credits) under the supervision of a professor or Faculty Lecturer; this requires the submission of a research project report evaluated by the supervisor (see guidelines). In this context, there might also the possibility to collaborate with research groups across campus that have software development or data analysis needs that require computer science expertise.

  • Secondly, they can conduct a 4-month industrial research internship (15 credits). Details about this internship course will soon be available online. Internships can be paid. Students will be registered full-time during their internship.

  • Thirdly, they can take additional courses to fulfill the 45-credit requirement.

The research project or the internship will likely be done in the summer.

We would like to note that students must apply for internships themselves and we cannot guarantee that they will be able to secure an internship that fits our course requirements for this internship. Similarly, students need to reach out themselves to potential supervisors for their research projects (they will do this in the Fall or Winter semester). While the School is committed to offer research projects to all students who wish to do so, their background must match the minimum requirements of the research projects offered.

The Office of Graduate studies together with the School of Computer Science will offer a seminar series held throughout Fall and Winter to prepare students for their research project / internship. Currently, this seminar is not mandatory but highly recommended for anybody who is interested in pursuing one of these two options.

Progress Tracking

Each student is assigned an academic advisor that oversees progress in the student's studies. Progress forms must be filled out on a regular basis and submitted to our Student Advising Supervisor.

Typical Timeline

The timeline below depicts the scenario where the student conducts a research project or an internship*.

Please note that international students must take at least 12 credits per semester for each but the last term they are registered to maintain full-time student status.

First semester (Fall-1):

  • Meet with program advisor to design Masters plan and make course selection.
  • Take 2 or 3 complementary courses (6-12 credits).
  • Take COMP 602 (1 credit)
  • Attend the Internship/Research Project Seminar Series in preparation for Internship / Research Project.

Second semester (Winter-1):

  • Take 2 or 3 complementary courses (6-12 credits)
  • Take COMP 603 (1 credit)
  • Attend the Internship/Research Project Seminar Series in preparation for Internship / Research Project.
  • Identify the project’s supervisor and initiate discussion on research topic OR
  • In preparation for the internship course prepare an application package and apply at relevant companies / organizations.

Third semester (Summer-1):

  • Carry out research project under the supervision of a professor or conduct internship.

Fourth semester (Fall-2):

  • Take 2 or 3 complementary courses (6-12 credits)
  • Prepare and submit research project or internship or report.

If students choose the course option, then they will not register for the summer semester. Instead, they will likely require a further Winter semester but have the summer off. We then recommend that they take 3 courses each semester in their first year (9-12 credits) and COMP 602/603 (1 credit each), and then distribute the remaining 17-24 credits across the second year. Students can also attempt to take 15 credits in each of three semesters (Fall/Winter/Fall) to complete the program in 3 semesters. However, this might be a very challenging workload at the graduate level.

Note that all M.Sc. students have a minimum of 3 semesters and a maximum of 3 years to complete their degree. If you have exceeded the 3 year maximum, you will have to apply for readmission.


In order to guide students in their choices, we suggest them to take a majority of courses from one of two streams listed below.

A stream in Machine Learning offers an in-depth coverage of both fundamental and applied concepts relevant in AI and machine learning. A stream in Software and Computer Systems provides students with the building blocks and technical skills needed for the development of large scale and complex software systems.

Note that specializations will not appear on a student’s transcript and are simply intended to provide guidance for course selection.

Stream 1: Machine Learning

COMP 514 - Applied Robotics
COMP 549 - Brain-Inspired Artificial Intelligence
COMP 550 - Natural Language Processing
COMP 551 - Applied Machine Learning
COMP 558 - Fundamentals of Computer Vision.
COMP 562 - Theory of Machine Learning
COMP 565 - Machine Learning in Genomics and Healthcare
COMP 579 - Reinforcement Learning
COMP 585 - Intelligent Software Systems
COMP 588 - Probabilistic Graphical Models
COMP 597 - Automated Reasoning with Machine Learning
COMP 597 - Applications of Machine Learning in Real World Systems
COMP 598 - Machine Learning for Biomedical Data
COMP 599 - Network Science
COMP 599 - Natural Language Understanding with Deep Learning
COMP 611 - Mathematical Tools for Computer Science.
COMP 766 - Learning and Optimization for Robot Control
COMP 767 - Machine Learning Applied to Climate Change

Stream 2: Software and Computer Systems

COMP 512 - Distributed Systems
COMP 520 - Compiler Design.
COMP 521 – Modern Computer Games
COMP 523 - Language-based Security.
COMP 525 - Formal Verification
COMP 529 - Software Architecture.
COMP 533 - Model-Driven Software Development.
COMP 535 - Computer Networks
COMP 547 - Cryptography and Data Security
COMP 555 - Software Privacy
COMP 585 - Intelligent Software Systems
COMP 596 - Principles of Computer Systems
COMP 599 - Software Engineering for Building Intelligent Systems
COMP 599 - Topics in Mobile Application Development
COMP 614 - Distributed Data Management
COMP 667 - Software Fault Tolerance
COMP 764 - Advanced Topics Systems

Admission requirements

Interested prospective applicants are encouraged to verify their eligibility on the M.Sc. Admissions requirements page and to view the program application deadline .


For any specific questions, see contact information here.