3.0

credits

Average Course Rating

This course introduces substantial machine learning methods of particular relevance to humanistic scholarship. Areas covered include standard models for classification, regression, and topic modeling, before turning to the array of open-source pretrained deep neural models, and the common mechanisms for employing them. Students are expected to have a level of programming experience equivalent to that gained from AS.360.304, Gateway Computing, AS.250.205, or Harvard’s CS50 for Python. Students will come away with an understanding of the strengths and weaknesses of different machine learning models, the ability to discuss them in relation to human intelligence and to make informed decisions of when and how to employ them, and an array of related technical knowledge.