This training workshop will take place before the main conference. It will be given in a small classroom, to maximize interaction and so you can ask even more questions than in a conference setting.IMPORTANT:- A specific ticket is required to get access to the workshop ("Workshop WORKSHOP_NAME + Conference 24-26/6").
- The venue is different from that of the main conference. The workshop will be held at Dafiti — many thanks to them for providing the space!
- Students are invited to bring their own laptops, if they want to replicate the hands-on demo (optional — see program below).
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SUMMARYManagers have a key role to play to make Machine Learning (ML) work for any organization. This non-technical but practical workshop is the first to be designed for operational and technical managers with no prior knowledge of ML. It provides the missing piece to identify the best opportunities with Machine and Deep Learning (DL) technologies, to apply them in your own projects, and to generate business value from data.
This workshop is focused not on teaching algorithms, but on how to make them work in the real world, and on key knowledge to start managing ML projects effectively. It will bring you up to speed on core ML concepts, illustrated with example use cases, and it will demystify ML/DL with a hands-on demo of a point-and-click and automated tool to compete in a Data Science challenge. You will learn how to set up and structure a successful ML project, and how to set direction for your team's work, with the help of the
Machine Learning Canvas. You will apply your learnings in a team exercice, where you will collaborate with others to create an MLC.
If you are a Manager, you will save months of work for your whole team by understanding the principles taught in this workshop. If you are a Developer or a Scientist, this is the workshop to recommend to your Manager, to better understand what today’s ML techniques can/cannot do, and how.
BONUS — Free ebooks for all participants: Bootstrapping Machine Learning and The Machine Learning CanvasTARGET AUDIENCEOperational managers, technical managers and technical decision-makers
LEARNING OBJECTIVES- Understand the unique opportunities ML creates, and its limitations.
- Design domain-specific evaluation procedures and performance metrics.
- Learn how to use the ML Canvas to frame ML problems, design real-world ML systems, and set up your own ML projects.
PROGRAMPossibilities and limitations of ML
- Core concepts of supervised learning: classification and regression
- Categorization of use cases and business applications; examples
- Preparing data for ML: from data collection to feature engineering
- Why/when ML fails
Hands-on demo: competing in a Kaggle challenge without coding
- Creating the best model for your data with “auto ML” techniques on BigML
- Making predictions with the model
- Submitting to Kaggle
Evaluating ML systems
- Using problem knowledge to design test datasets properly
- Designing domain-specific performance metrics
Formalizing ML problems
- Bridging the gap between predictions, decisions, and value
- Specifying ML systems with the Machine Learning Canvas
- Application of the Canvas to example ML use cases (team exercice)
Conclusions
- Recap of key takeaways
- Where to go from here
GET YOUR WORKSHOP TICKET NOW!
