| Abstract |
The quantified self is any individual engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information. The self-tracking is driven by a certain goal of the individual with a desire to act upon the collected information. Nowadays, the amount of measurements devices and data that results from these devices is overwhelming. Just think of the amount of information a smart phone generates in terms of location, phone usage, accelerometer data, etc. Machine learning approaches can help to find interesting patterns in the data and support a user (or quantified self-er) to act upon these insights effectively, thus contributing to the goal of the user. In this talk, I will present an overview of state of the art machine learning concepts for this purpose, and the challenges posed by the setting of the quantified self. It is based on a book I am writing together with a colleague. |