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, you don’t need to know how to code, the entire course is done with pens, paper and your brains!
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 participants will be challenged to build their own machine learning algorithm for a set of real world data. This will test their application of statistical knowledge to examine the effects of different decisions on the outcome. By analysing a number of real world applications of machine learning participants will gain analytical skills to evaluate not just the benefits, but also the limitations of machine learning and AI on different types of data.
Best of all, you don’t need to know how to code, the entire course is done with pens, paper and your brains. A laptop might help you wrap your head around the data in your solutions to the various puzzles presented on the course.
Our partners most popular course
This course has become so popular with our partners and clients that we have yet to offer a public instance. Within the Civil Service Learning programme we offer a variant for policy makers, while in the BBC we have been delivering this course to members of staff (at all levels) involved in using data to inform decision making. Averaging one instance a week for a three month period at the end of 2018.
Is this course just for developers?
This 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:
Describing 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
The ODI was co-founded in 2012 by the inventor of the web Sir Tim Berners-Lee and artificial intelligence expert Sir Nigel Shadbolt to show the value of open data, and to advocate for the innovative use of open data to affect positive change across the globe.
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