Artificial Intelligence Engineering
In our Artificial Intelligence Engineering* program you will learn the ethical implications of the use of AI in engineering and society, including issues related to bias, privacy and the impact of AI on employment and decision-making. Through experiential learning opportunities, students will gain experience in designing, implementing and deploying AI models using industry-standard tools and frameworks. This program equips graduates with the skills needed to innovate and lead in the rapidly evolving field of AI, preparing them for a variety of roles in academia, industry and research.
You can also choose to do a specialization in Artificial Intelligence within the Mechanical Engineering and Mechatronics Engineering programs.
*Pending ministry approval
Consider AI Engineering if you find yourself asking questions such as:
- What makes a self-driving car able to "see" the road and make decisions?
- How will robots and AI work together in factoris, hospitals, or homes of the future?
- How can I turn a pile of data into something a computer can actually learn from?
- How do large language models (LLMs) and other AI systems actually work?
- If a model is only as good as the data it learns from, how do we know when that data is good enough?
Learn how popular AI systems process information and "think."
Turn huge piles of data into smart choices and pathways that computers can learn from.
Hands-on experience building AI for real-world tech for self-driving cars and robots.
After graduating you can...
- Ensure AI is fair and safe for everyone.
- Discover new ways for computers to think and solve problems.
- Turn massive amounts of data into smart solutions.
- Build systems that learn on their own.
- Teach computers to understand and speak human language.
- Develop smart software and apps for global companies.
...and many more!
Sample Courses:
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Deep Learning Architecture and Algorithms
This course provides an in-depth exploration of deep learning techniques and their applications in modern artificial intelligence systems, with a strong focus on the underlying algorithms that drive these models. Students will study neural network architectures, including feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative adversarial networks (GANs), examining the mathematical foundations and algorithmic strategies behind their design and training. The course covers essential topics such as gradient-based optimization methods, backpropagation, regularization techniques, and scalability of deep learning models. Emphasis is placed on practical implementation using deep learning libraries and frameworks, enabling students to build and deploy deep learning models for tasks in computer vision, natural language processing, and reinforcement learning. Ethical considerations and the limitations of deep learning are also discussed to foster responsible AI development.
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AI Engineering and Machine Learning Operations
This course focuses on the principles and best practices for deploying, managing, and maintaining AI and machine learning systems in real-world applications. Students will explore the full lifecycle of machine learning models, from data preprocessing and model training to deployment, monitoring, and continuous integration. Key topics include cloud-based AI services, containerization, model versioning, automated pipelines, and scalable deployment platforms. The course emphasizes reliability, reproducibility, and ethical considerations in AI engineering, ensuring that students gain hands-on experience with industry-standard MLOps tools. By the end of the course, students will be able to build, deploy, and maintain robust AI solutions that align with software engineering best practices.
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Foundation Models and Generative AI
This course explores the principles, architectures, and applications of foundation models and generative AI, including large language models (LLMs), diffusion models, and multimodal AI systems. Students will study the underlying techniques behind models such as GPT, BERT, and Stable Diffusion, focusing on training methodologies, fine-tuning strategies, and deployment considerations. The course covers key topics such as transfer learning, prompt engineering, ethical implications, and real-world applications in text, image, and code generation. Through hands-on projects, students will gain practical experience in building, customizing, and integrating generative AI models into software systems, preparing them for the rapidly evolving AI landscape.