Master of Engineering (MEng) with AI Concentration
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Concentrating on the forefront of intelligent technology, our Master of Engineering (MEng) program specializing in Artificial Intelligence (AI) will provide you with the technical training and ethical awareness to make a meaningful impact in the AI field. In this full-time program, you will explore the inner workings of AI-driven systems, analyze state-of-the-art AI techniques and learn how to apply them in a range of disciplines. Recognized by the Vector Institute (external link, opens in new window) , the program consists of four core courses – Intelligent Systems, Neural Networks, Deep Learning and Advanced Data Engineering – and interdisciplinary electives related to energy, sustainability, computer networks, digital media and urban development.
The program’s current capacity is 50 full-time students per year. Admission is based on excellence and the following requirements:
- Completion of a four-year bachelor’s degree in a related field.
- Minimum GPA or equivalent of 3.00/4.33 (B) in the last two years of study.
- A statement of interest in AI specialization demonstrating a capacity to succeed in the program.
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The Vector Scholarship in Artificial Intelligence supports the recruitment of top students to AI-related master's programs in Ontario. To find out how and when to apply please visit "The Vector Scholarship in Artificial Intelligence (external link) " website.
Upon completion of the program, you will be able to contribute solutions to many of industry and society’s greatest challenges. Specifically, you will gain a deep understanding of:
- Foundational aspects of machine learning, from data mining to statistical pattern recognition, including the core techniques in supervised and unsupervised classification.
- Theoretical foundations and practical applications of artificial neural networks, including various forms of representation, training and evaluation.
- State-of-the-art techniques in the area of deep learning, including deep convolutional neural networks, recurrent neural networks and deep belief networks.
- Actual applications of machine learning and statistical pattern recognition, including use cases in audio and video processing and natural language processing.
- How big data needs to be modelled, stored and retrieved within the context of distributed computing using MapReduce, Hadoop, Spark, NoSQL technology and distributed stream-processing techniques.
- How to develop scalable and distributed data management platforms that are essential for large-scale data analytics and machine learning projects.
- Inherent biases of machine learning algorithms, algorithmic fairness, as well as accountability and transparency in AI systems.
The field of AI offers highly compensated, impactful employment opportunities across a variety of sectors. Graduates of our program can expect to be qualified for many in-demand positions, including:
- Machine learning engineer
- Data scientist
- Research scientist
- R&D engineer
- Business intelligence developer
- Computer vision engineer
- Big Data engineer/architect
Combining technical theory, experiential learning and applied research, the program’s practicum gives you the opportunity to conceptualize, develop and test a prototype solution for a real-world problem. The project will be completed in teams of two to three students who work directly with their professor and industry partners. You will also benefit from department-led training in teamwork, collaboration and conflict resolution – skills that will be essential to your success in the workforce.
If you have questions about the program, email gradinfo@ecb.torontomu.ca. For more information about other graduate studies within the Faculty of Engineering and Architectural Science, visit our Graduate studies page.