
Explore how ai, machine learning, and data literacy transform ocean work, with ethical, practical guidance for upskilling your organization toward ai readiness.
Differentiate artificial general intelligence from artificial narrow intelligence, then explore machine learning, neural networks, and deep learning that power natural language processing and generative AI, with attention to ethical issues.
Explore what machine learning is and how it works in ocean science, covering supervised, unsupervised, and reinforcement learning, as well as deep learning and neural networks.
Explore how data, statistics, and algorithms underpin machine learning, distinguish structured and unstructured data, and highlight high-quality data to train accurate marine species models.
Explore how supervised learning trains models with labeled data to learn patterns, classify or predict outcomes in classification and regression tasks, and assess accuracy using unseen data.
Explore unsupervised learning, where models discover patterns in unlabeled data through clustering and dimensionality reduction, revealing structure without guidance.
Compare reinforcement learning, ensemble learning, and supervised versus unsupervised learning, and explore machine learning limitations like data quality and black box explainability.
Explore neural networks and deep learning, from layered architectures to training data, and see how they power ocean analytics, climate models, and generative AI while addressing bias and explainable AI.
Assess your next steps in ai readiness and learn frameworks to evaluate feasible projects. Adapt learning paths to your context and cultivate lifelong learning habits for ongoing ai engagement.
Build AI readiness and a lifelong learning mindset by strengthening data literacy, exploring data science foundations, and practicing prompt engineering while evaluating ethics, privacy, and bias in AI tools.
Identify AI opportunities in your organization and assess feasibility to drive meaningful, low-risk outcomes. Strengthen expertise, data quality, and infrastructure, while promoting AI literacy and ethical governance across stakeholders.
What discoveries can be made, if we can just connect the dots?
A 90-minute, asynchronous, self-paced virtual course for professionals and students in the ocean sectors of Canada, presented in conjunction with the Building Bridges project. Course 1 acts as a starting point for learning about how to engage safely, responsibly, and ethically with artificial intelligence (AI), in professional contexts and more broadly.
Learners will be presented with the basic concepts that underlie the modern emergence and applications of AI tools and technologies through video lectures, external resources, examples and case studies pertaining to ocean science contexts, and periodic quizzes and self-directed activities for practice and assessment.
The outcomes of the course are related to the fostering of a growing AI literacy on an individual and sectoral basis, which will better equip individual learners and organizations to navigate the dynamic landscape of AI, its promise, and its problems; it will help establish some common conceptual frameworks, terminology, and baseline knowledge that are the first steps in cultivating an **Ocean AI Community of Practice**. It will also aim to connect learners with clear next steps for a diverse range of AI learning journeys and needs.
We start with an exploration of what exactly AI is, and what it means. In Module 2, you will learn the basics of Machine Learning (ML), starting with the foundational data science concepts upon which ML is built, and continuing with a survey of some of the most common kinds of ML tools. Finally, in Module 3, we will discuss how you might continue your AI learning journey as an individual, and what AI readiness looks like in the context of an organization, as well as guidance for pursuing AI projects.