This is a two-day course for professionals who can, or already have, built data-driven models. The course aims to develop and enhance the technical skills necessary for building systems that use machine learning to make automated decisions whilst accounting for ethical objectives.
Outcomes: By the end of the course, participants will have explored, tested and scrutinised simple model systems that rely on machine learning to make automated decisions whilst also accounting for ethical objectives. Participants will understand and delved into some of the technical pitfalls that prevent machine learning systems from behaving ethically, and how to identify and correct for these. While many of the concepts discussed in this course are applicable across a wide range of AI systems, the course primarily focuses on models built using structured and labelled training data.
Prerequisites: This course is for people who have experience building data-driven models, interpreting graphs and are comfortable discussing terms such as “parameter optimisation”, “overfitting” and “model validation”. Exercises and activities are based on interactive models and visualisations; however, no coding is needed during the course.
Format: The course is run in classes of up to 15 participants and led by two instructors from Gradient Institute’s team of machine learning specialists. At the start of each topic, there is a short presentation on key concepts, followed by class discussion. Participants also learn by working through exercises and examples in open-source Jupyter Notebooks. The notebook solutions and the presentation material are provided after the course as a reference.
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