Queen's University

Overview

The Master of Applied Science (MASc) in Artificial Intelligence at Queen's University equips students with the skills to design and evaluate AI and machine learning methods applicable to complex research and real-world challenges. The program emphasizes understanding the ethical and societal implications of AI through a research-informed, inquiry-based learning approach.

What You'll Study

The curriculum combines classroom instruction, online learning, seminars, and faculty-supervised research to foster a comprehensive understanding of AI. Students engage in inquiry-based learning, emphasizing teamwork and problem-solving through various projects.

Core Coursework

• Signal Processing

• Machine Learning and Deep Learning

• Artificial Intelligence and Interactive Systems

• Deep Learning in Computer Vision

• AI for Cybersecurity

• Wearable and IoT Computing

Research and Academic Development

• Research seminars focusing on literature review, research design, and scholarly communication

• Faculty-supervised research projects as part of the MASc research component

• Collaborative course projects and problem-solving activities to enhance applied skills

Note: The availability of courses and the specific combination of required and elective courses may vary based on term and research focus.

Entry Requirements

Applicants are typically expected to hold an undergraduate degree in a relevant field, such as computer science, engineering, mathematics, or a closely related discipline, and possess a solid foundation in programming and quantitative methods. There are no specific GRE or GMAT requirements for this program, and GPA standards are not explicitly stated.

Career Prospects

Graduates of the MASc in Artificial Intelligence are well-prepared for a range of career opportunities in various sectors, including technology, healthcare, finance, and research. The skills acquired in this program enable students to tackle complex problems, innovate solutions, and contribute to advancements in AI and machine learning.