Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data

Identifying behavioral structure from deep variational embeddings of animal motion

The Quantified Self

Narrowing Reinforcement Learning: Overcoming the Cold Start Problem for Personalized Health Interventions

Burkhardt Funk - Böcker

Frontiers Learning of Artificial Sensation Through Long-Term Home Use of a Sensory-Enabled Prosthesis

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.

A First Course in Machine Learning [Book]

Overview – Machine Learning for the Quantified Self

Source code – Machine Learning for the Quantified Self

Dissecting neural computations in the human auditory pathway using deep neural networks for speech

Ecological Momentary Assessment in Mental Health Research

Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data