Technical service highlights
Machine learning is the ability of computers to identify patterns, learn from data, and make inferences or decisions, without having been explicitly programmed to do so. This exciting field is a key part of artificial intelligence, driving innovation in research methods, industrial operations, and consumer products like mobile devices and smart homes.
The NRC excels at machine learning to query large volumes of text, perform machine translation and evaluation, uncover new molecular interactions, monitor engineering systems, and capture data through machine vision.
What we offer
Machine learning experts in the Digital Technologies Research Centre offer the following competencies and services to their collaborators:
- Data analytics
- Learning for language processing and computer vision
- Neural networks and deep learning
- Reinforcement learning
- Supervised, unsupervised and semi-supervised learning
Why work with us
Our experts have over 20 years of experience collaborating with industry, government, and academia to develop new machine learning technologies. We can help you understand which types of machine learning are most suitable for your project and develop technologies that help you reach your research goals.
If you're interested in accessing our machine learning expertise or collaborating with us, contact us today!
Our experts are based across Canada at the following NRC locations and NRC collaboration centres on university campuses and institutes:
- Fredericton: CIC-NRC Cybersecurity Collaboration Consortium
- Montreal Decelles
- Ottawa Montreal Road
- Toronto: NRC-Fields Mathematical Sciences Collaboration Centre
- Waterloo: NRC-Waterloo Collaboration for AI, IoT, and Cybersecurity
- Mapping the global design space of nanophotonic components using machine learning pattern recognition
- Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks
- Multiclass nonnegative matrix factorization for comprehensive feature pattern discovery
- Deep feature selection: Theory and application to identify enhancers and promoters
- Long short-term memory over recursive structures