About the course
Learn to apply machine learning and AI techniques to data and discover how ethical frameworks can help you avoid teaching your machines bad habits.
This course is essential for anyone needing a theoretical understanding of the opportunities and limitations of using machine learning on data.
The course takes a practical approach to understand the key machine learning techniques, how they can be applied and what implications each has.
Best of all, the course is designed to be non-technical. All practical exercises use drag and drop interfaces with virtual pens, post-its and paper.
Why is the ODI running this course?
At the ODI, we believe that fostering AI innovation requires an open approach that includes open data, open source code and open culture. This is essential because algorithms in autonomous and machine learning systems need large quantities of high-quality data to perform well.
Currently, most AI systems are generally provided as inscrutable ‘black boxes’ with no knowledge of their internal workings. This is problematic: such business model homogeneity can have a chilling effect on innovation and stall a thriving AI sector.
The course takes a practical approach to understand the key machine learning techniques to help you understand what these black boxes might be doing, how they can be applied and what implications each has. During the course you will be challenged to build your own machine learning algorithm for a set of real world data. This will test your application of statistical knowledge to examine the effects of different decisions. By analysing a number of real world applications of machine learning you will gain analytical skills to evaluate not just the benefits, but also the limitations of machine learning and AI on different types of data. The course wraps up with a discussion on how ethical frameworks can help avoid teaching your machines bad habits.
Is this course just for developers?
No!
This non-technical course is essential for anyone needing a theoretical understanding of the opportunities and limitations of using machine learning on data. This includes (but isn’t limited to) project leaders, C-suite managers, statisticians, data analysts, data scientists and developers.
What you will learn
By the end of the course you will be able to:
- Describe the key knowledge and skills to engage in machine learning, AI and ethics
- Identify applications of machine learning
- Examine shapes and trends in data
- Apply a machine learning technique to real world data
- Evaluate the risks of using machine learning without statistical knowledge
- Analyse the limits of applications of machine learning data
- Analyse the implications of using big data
- Analyse the ethical risks of making automated decisions from data