Solving problems with the k-NN classifier

Last month's k-nearest neighbour implementation workshop, but we'll recap how a k-NN classifier works so no previous knowledge required.

This time, we'll look at how you'd use a kNN classifier to solve problems. We'll look at how we could visualise and prepare data before we train and test the classifier. Then we'll use the results of our testing to try again, changing details to try and improve the performance.

We'll focus on a tried-and-tested implementation like Python's scikit-learn kNN classifier (http://scikit-learn.org/stable/modules/neighbors.html) but you're welcome to use any language and classifier you like, including one you've built yourself.

We may also have a couple of short "lightning talks" from group members about how they're using or planning to use machine learning!

Venue

Conference Room 2 in the Showroom Workstation.

What to bring

You'll need a laptop that you can use to run your classifier. If you're not sure how to set up for that, come along at 18:30 and we'll help you get set up.

Important to know

Everyone involved with SheffieldML is asked to abide by a code of conduct that boils down to treating everyone with respect. Specifics are available at http://confcodeofconduct.com/