Working with Algorithms

A class on interpreting high-dimensional data, clustering, and document vector space model

A class on text analysis in journalism, probabilistic topic modeling, matrix factorization, more general plate-notation graphical models, and word embedding approaches based on deep learning

A class on filtering algorithms, the approaches used by social networks, and effects such as polarization and filter bubbles

An overview class on what to count, how to “interview the data,” statistical models, the uses of multi-variable regression in journalism, and correlation vs. causation

A class on algorithmic accountability and discrimination

A class on algorithmic fairness, definitions of fairness and "bias"

A class on causality, p-hacking, reproducibility, and triangulation

A class on visualization (built upong design principles from user experience considerations, graphic design, and the study of the human visual system) and social network analysis in journalism

A report on algorithms, the idea of algorithmic accountability, reverse engineering algortihms, and methodological details on how algorithmic accountability reporting might be realized in practice