In an increasingly data-driven world, artificial intelligence is expected to be a key tool for converting big data into tangible benefits and the healthcare domain is no exception to this. Machine learning aims to identify complex patterns in multi-dimensional data and use these uncovered patterns to classify new unseen cases or make data-driven predictions. In recent years, deep neural networks have shown to be capable of producing results that considerably exceed those of conventional machine learning methods for various classification and regression tasks. In this paper, we provide an accessible tutorial of the most important supervised machine learning concepts and methods, including deep learning, which are potentially the most relevant for the medical domain. We aim to take some of the mystery out of machine learning and depict how machine learning models can be useful for medical applications. Finally, this tutorial provides a few practical suggestions for how to properly design a machine learning model for a generic medical problem.

Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases in silico with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer’s disease affecting the visual cortex. We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. Simulated PCA impacted learned representations hierarchically, as networks lost object-level representations before category-level representations. Incorporating this paradigm in computational neuroscience will be essential for developing in silico models of the brain and neurological diseases. The paradigm can be expanded to incorporate elements of neural plasticity and to other cognitive domains such as motor control, auditory cognition, language processing, and decision making.

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This annotated playlist follows Missy Elliott's musical cinema. Paying particular attention to the use of speculative tropes, it argues that Elliott's work draws on Black feminist aesthetics to paint paths through memory, nostalgia, sexual autonomy, and spiritual rebellion. This mélange of speculative and sonic aesthetics creates what I call reverberative memory: a polytemporal structure that uses music and performance to tap into intergenerational memory. Using the aesthetic markers of speculative genres, Missy Elliott's videos fuse hip-hop to the conjuring of the blues, providing a generative commentary on sexuality, Black femininity, gender nonconformity, and pleasure. This critical layering of the strange and uncanny provide a utopic space in which Black feminist life thrives. The methodological focus of this essay draws on an array of Black scholarship that maps historical formations of blues aesthetics in order to address Black women in the horror genre; constructions of gender, aesthetic, and sonic mapping that exists in the music video form; and the spiritual ontology of nostalgia. Chiefly, this playlist highlights the pioneering art of Missy Elliott across a body of work that centers the vibrancy and vitality of Black women.