Master of Computer Science in Applied Artificial Intelligence

On-campus

Location

All on-campus programs are delivered at the University of Niagara Falls Canada campus in downtown Niagara Falls. Students attend in-person classes, access labs, collaborate with peers, and engage directly with faculty. Certain programs include specialized equipment and lab components, which are provided by the university.

Schedule

Courses follow a structured weekly schedule. Most classes use a flipped-classroom model, meaning students complete pre-class learning and then apply concepts through active, in-person sessions. Expect approximately two hours of in-person class time per course each week, with additional guided activities, labs (where applicable), and independent study. Exams for on-campus courses are conducted in person.

Timeline

UNF follows a four-term academic year. Terms consist of 10 weeks of instruction, followed by an additional final evaluation period that is typically one to two weeks.

Break term: One scheduled break term is included annually.

Study terms: Students typically complete three study terms each year.

Graduate programs are structured for 18-month completion, while undergraduate programs follow a four-year honours pathway. Students may take longer within the approved maximum duration for their program.

Course content

Each course includes a detailed syllabus outlining learning outcomes, weekly topics, assessments, and required materials. Coursework blends theory with applied learning, supported by case studies, simulations, hands-on activities, and real-world assignments. Some programs also include labs or specialized equipment, all provided by UNF.

Participation

On-campus learning is highly interactive. Students engage through discussions, group projects, presentations, and applied challenges. Collaboration is central, approximately a 60/40 split between individual and group assessments is typical. Students also meet with instructors during scheduled classes and can arrange additional meetings as needed. Faculty integrate real-world tools, AI applications, analytics platforms, and industry-focused technologies into classroom activities.

Assessments

While individual courses vary, depending on the subject matter and instructor, students can expect a mix of quizzes, exams, presentations, case studies, projects, and applied assignments. Undergraduate programs generally include more quizzes and exams, while graduate programs emphasize applied work such as projects, research, and capstones. Assessment weights vary by course, typically ranging from five to 50 percent per component.

Learning environment

Studying on campus provides direct access to faculty, student services, academic advising, and the Work-Integrated Learning (WIL) Office. Students form networks through peer collaboration, challenge-based learning, and internship or practicum opportunities (where applicable). Capstone projects are completed under faculty supervision as the culminating applied component of each master’s program.

Technology and resources

On-campus students use the same learning technologies as online learners, including D2L Brightspace and industry-relevant digital tools. Additional equipment or lab access is provided for programs that require it. Students benefit from a full suite of on-campus supports, including tutoring, writing assistance, and program hubs.

Online

Location

This is a fully online program. You can complete your coursework from anywhere.

Schedule

There will be no scheduled classes. Asynchronous learning means you can study at your own pace. All course materials will be made available at the start of term, except for exams and quizzes.

Timeline

Weeks 1-10: You will be able to access your course content online and complete tasks throughout this period.

Weeks 11-12: You will need to complete any review activities and final assessments, including exams and final projects.

Course content

Each course will have a syllabus as well as additional course materials that will be made available at the start of the term, except for exams and quizzes.

Participation

While components vary depending on the individual course, you can expect to use discussion forums and group chat platforms, participate in videoconferences, as well as utilize project management tools. There will be group assignments in addition to individual assignments.

Term two courses

Computational Linguistics and Natural Language Processing
An introduction to the fundamentals of natural language processing (NLP), covering syntax, semantics, and pragmatics. Students will learn to develop NLP applications for tasks such as question-answering, sentiment analysis, translation, summarization, and chatbot creation. The focus is on enabling computers to understand and generate human-like language.

Foundations of Large Language Models
A primer on modern language modeling techniques and their practical applications, with a focus on transformer architecture and attention mechanisms. Initially, it delves into the probabilistic underpinnings of language models from a formal, theoretical angle and progresses to construction, training, and fine tuning of large-scale neural network models. Additionally, it covers systems programming, privacy concerns and societal impacts of AI including bias and misinformation.

Elective Course One
Choose one course from a list of electives, based on availability.

Term three courses

Image Processing and Pattern Recognition
This course empowers students to grasp the fundamentals of image processing and pattern recognition, enabling them to create software for automated analysis and interpretation of images and videos. It covers a comprehensive exploration of image processing techniques and theories, including spatial filtering, morphological processes, segmentation, and object recognition.

Advanced Topics in Artificial Intelligence
Exploring the forefront of artificial intelligence (AI), students will examine cutting-edge topics, methodologies, and applications shaping the field. Students will focus their attention on AI advancements in their chosen domain in preparation for their internship experience and capstone project. 

Elective Course 2
Choose one course from a list of electives, based on availability.

Term five courses

Ethical Considerations with AI
Explore the ethical, social, and cultural implications of artificial intelligence (AI). This course examines the challenges AI presents to individuals, organizations, and society, focusing on issues like bias, fairness, privacy, and the impact on employment. Students will analyze real-world case studies and consider ethical frameworks for responsible AI development and deployment.

Internship 
The Applied Artificial Intelligence Internship combines structured learning and experiential learning and provides the student the opportunity to apply the knowledge acquired during the program to a real-world setting.

Term six course

Capstone Project
Students complete a major AI consulting project with a client, focusing on project initiation, planning, execution, monitoring, controlling, and closing, guided by a faculty advisor.