[Free] Building Bridges: Intro To Ocean Ai And Ai Literacy
Ocean AI, AI literacy, Machine Learning, and AI Readiness – Free Course
What you’ll learn
- Define and distinguish between basic AI concepts, terminology, and kinds of tools
- Describe basic processes and use cases for machine learning and deep learning
- Identify the skills and competencies associated with AI literacy
- Identify and self-assess components required for personal and organizational AI readiness
- Identify ocean sector applications of AI tools
Requirements
- No technical experience required. Familiarity with basic data science concepts will be beneficial, but not requisite.
Description
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.
Author(s): CIOOS Atlantic
