Concentrations | Computer Science | Memorial University of Newfoundland

⚠︎	Concentrations become available from Fall 2025. Students who graduate before Winter 2026 convocation cannot secure a concentration designation on their transcript.

An opportunity to specialize

Completing a concentration allows you to demonstrate specialization within the field of computer science. Concentrations can be completed by students in the Majors and Honours programs. Students can complete more than one concentration.

Each concentration requires the completion of 18 credit hours of courses, some required, some optional as listed below.

⚠︎	Some concentrations allow non-computer science courses as electives. Please note that such courses will not count towards the computer science requirements of your degree.

When applying to graduate, a student who has completed a concentration can write to the Registrar to request that a concentration designation appears on their transcript.

We offer concentrations in these areas:

Artificial Intelligence

Data-centric Computing

Theory of Computation

Visual Computing and Games

Artificial Intelligence

The AI concentration gives an overview of the growing body of algorithmic and mathematical techniques that have proven practical in allowing computer systems to deal intelligently with the complexities and uncertainties of both human beings and the real world in an efficient and robust manner.

Core (both required)

COMP 3200: Algorithmic Techniques for Artificial Intelligence

COMP 3202: Introduction to Machine Learning

Electives (four required)

COMP 3201: Introduction to Nature-Inspired Computing

COMP 3401: Introduction to Data Mining

COMP 3766: Introduction to Robotic Manipulation

COMP 4301: Computer Vision

COMP 4303: Artificial Intelligence in Computer Games

COMP 4750: Introduction to Natural Language Processing

COMP 4766: Introduction to Autonomous Robotics

499A/B: Honours Project (provided the honours thesis is in the field of Artificial Intelligence)

Statistics 4486 Neural Networks and Deep Learning

Data-centric Computing

Data is essential in today’s industries, science and government, and computer scientists capable of working with data are in high demand. Data plays a vital role in decision making in many areas such as social sciences, business, biomedical science, and government policy. Our Data-centric Computing stream is intended for students who wish to pursue a major in computer science with a focus on data processing and analysis techniques at the higher level of their studies. The stream will prepare computer science students to deal with data at every step of the data processing cycle to transform data into useful information.

Core (all required)

COMP 3400: Data Preparation Techniques

COMP 3401: Introduction to Data Mining

COMP 4304: Data Visualization

COMP 4754: Database Systems

Electives (two required)

COMP 3202: Introduction to Machine Learning

COMP 3550: Introduction to Bioinformatics

COMP 3731: Introduction to Scientific Computing

COMP 4550: Bioinformatics: Biological Data Analysis

COMP 4734: Matrix Computations and Applications

COMP 4750: Introduction to Natural Language Processing

499A/B: Honours Project (provided the honours thesis is in the field of Data-centric Computing)

Statistics 3530: Analysis of Observational Data

Statistics 4411: Bayesian Data Analysis

Statistics 4486: Neural Networks and Deep Learning

Theory of Computation

Students concentration in this field will explore the limitations and capabilities of efficient computation. This includes areas such as computational complexity, algorithm design and analysis, logic, and combinatorics.

Core (all required)

COMP 3600: Algorithm Design and Analysis

COMP 3602: Introduction to the Theory of Computation

COMP 4742: Computational Complexity

Electives (three required)

COMP 4741: Formal Languages and Computability

COMP 4743: Graph Algorithms and Combinatorial Optimization

COMP 4750: Introduction to Natural Language Processing

499A/B: Honours Project (provided the honours thesis is in the field of Theory of Computation)

Mathematics 3240, 3300, 3320, 3340, 3370, 4252, 4320, 4321, 4331, 4340, 4341, 4370 (please see the University Calendar for course titles and descriptions)

Visual Computing and Games

Visual perception is responsible for most of our impressions about the world around us. The visual computing field in computer science studies how to use computer to both mimic humans’ visual processing power (such as object recognition) and to create visual content (such as games and movies). Computer games offer a great opportunity for computer scientists to learn and apply fundamental concepts of the design and creation of interactive experiences and visual content.

The courses in the VCG concentration cover a variety of sub-fields that are related to visual computing and interaction, including image processing, computer vision, multimedia, and game development. The concentration has a strong emphasis on hands-on learning and exploration of applied aspects of visual computing and games. Students completing this concentration will be equipped with skills that will allow them to develop professionally in these visually oriented fields, and will also be prepared for graduate studies in computer graphics, vision and human-computer interaction.

499A/B: Honours Project (provided the honours thesis is in the field of Visual Computing and Games)