Machine Learning within an organisation

• Review the standard ML use-case cycle from “I have an idea” to “I can monitor my ML
  application running in production and quantify the benefits of having such a product”
• Translation of a business problem to a Machine Learning project. Review of the
  importance of defining the metrics of interest
• Review the modern way of running a ML project. Exploration of cloud services and in
  particular AutoML features allowing for fast prototyping
• Illustration of concepts using use-cases covered in previous modules, embedding in
  particular topics such as ML bias, cloud costs as well as regulatory and ethical topics.
  Overarching view of the ML product with emphasis on ROI.
• Business cases and practical examples with real data using the Machine Learning platform
  “BigML.com” and original data) – no programming required!

Top speaker

• Dr. Christopher Bruffaerts, University of Fribourg

Subject Area Co-ordinator

• Prof. Dr. Michael Burkert, University of Fribourg