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. They then apply the knowledge and gain hand-on experience through a 4-month industrial internship* or an academic research project. 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.
This 45-credit program can be completed in 16 full-time months (typically Fall/Winter/Summer/Fall).
In its current form students have to attend talks throughout the first year in the School’s Computer Science Seminar to get a broad insight of current research challenges (COMP 602 in Fall and COMP 603 in Winter). Furthermore, they take 7-8 complementary courses with a breadth requirement. Finally, they conduct a moderate-scale 4-month research project 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 is also the possibility to collaborate with research groups across campus that have software development or data analysis needs that require computer science expertise.
The detailed program description of the current program can be found here.
The School is currently revising the program so that the research project can be replaced by an internship or additional courses, offering three paths to completion: courses and a research project, courses and an industrial internship, or courses only. These changes will likely be in place for students starting in 2023. As these changes are not yet approved, the remainder of this text uses an * whenever the internship course is mentioned.
In all cases, the courses must meet the Breadth Requirement, namely courses must be from at least two of the three areas of Theory, Systems, and Applications (see course classification (extra link that lists the courses). 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.
Progress Tracking
In the second term of studies, students will meet with an assigned advisor and assess the progress made so far to see whether sufficient courses have been taken so far and the right courses for the fall semester are chosen. Furthemore, the student is advised in regard to the research project and the internship course* to ensure that a project topic or internship* is secured. A progress form must be filled by the student, discussed with the advisor, and signed by both. It must then be submitted to our Student Advising Supervisor.
Typical Timeline
The timeline below depicts the scenario where the student conducts a research project or an internship*.
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)
Second semester (Winter-1):
- Take 2 or 3 complementary courses (6-12 credits)
- Take COMP 603 (1 credit)
- In preparation for the 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.
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.
Streams
In order to guide students in their choices, we suggest them to take a majority of courses from one of two streams listed below. Note, however, that the program has a breadth requirement that requires students to take courses from at least two of the course categories Theory, Systems, and Applications. For the list of courses in each category see here.
A stream in Machine Learning and Data Science offers an in-depth coverage of both fundamental and applied concepts relevant in AI and machine learning and their applications for Data Science. 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 and Data Science
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 596 - From Natural Language to Data Science 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 598 - Data Science 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 ChangeStream 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 SystemsContact
For any specific questions, see contact information here.
