The book is currently available in draft form as a downloadable pdf. Every day we interact with machine learning systems that personalize their predictions to individual users, whether to recommend movies, find new friends or dating partners, or organize our news feeds. Such systems involve several modalities of data, ranging from sequences of clicks or purchases, to rich modalities involving text, images, or social interactions. While settings and data modalities vary significantly, in this book we introduce a common set of principles and methods that underpin the design of personalized predictive models. The book begins by revising "traditional" machine learning models, with a special focus on how they should be adapted to settings involving user data.

Robots have great potential to facilitate future therapies for children on the autism spectrum. However, existing robots lack the ability to automatically perceive and respond to human affect, which is necessary for establishing and maintaining engaging interactions. Moreover, their inference challenge is made harder by the fact that many individuals with autism have atypical and unusually diverse styles of expressing their affective-cognitive states. To tackle the heterogeneity in behavioral cues of children with autism, we use the latest advances in deep learning to formulate a personalized machine learning (ML) framework for automatic perception of the childrens affective states and engagement during robot-assisted autism therapy. The key to our approach is a novel shift from the traditional ML paradigm - instead of using 'one-size-fits-all' ML models, our personalized ML framework is optimized for each child by leveraging relevant contextual information (demographics and behavioral assessment scores) and individual characteristics of each child. We designed and evaluated this framework using a dataset of multi-modal audio, video and autonomic physiology data of 35 children with autism (age 3-13) and from 2 cultures (Asia and Europe), participating in a 25-minute child-robot interaction (~500k datapoints). Our experiments confirm the feasibility of the robot perception of affect and engagement, showing clear improvements due to the model personalization. The proposed approach has potential to improve existing therapies for autism by offering more efficient monitoring and summarization of the therapy progress.

Recent advances in machine learning have enabled a number of applications for health and well-being, marketing and social robots, among others. Traditional machine learning relies mainly on generic models: models tuned to an average target population. However, the'good' performance by these generic models doesn't necessarily translate to each individual in the group. While this can be acceptable in certain domains (e.g., marketing research), when it comes to, for instance, health and well-being, new systems need be optimized and work for each person. They should also help an individual to see, for example, which factors they might change in their life to improve their health or mood.