Machine learning methods for human-computer interaction [tutorial]

In this tutorial, I will cover various machine learning methods for pattern recognition at an overview level illustrated with case studies mostly taken from haptics applications, and further lay out the space covered by other methods without reviewing them specifically. I will only talk about basic statistical pattern recognition methods applied for supervised learning; namely, Bayesian decision theory, linear discriminant, and k-nearest neighbor methods; emphasizing the distinction between generative and discriminative approaches. I will close by mentioning commonly used extensions of the introduced methods and by providing resources for the participants to follow up with. I will also provide some guidelines on parameter selection and optimization for the classifiers, which is still a research problem in pattern recognition.