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Courses

2018 - 2019

2017 - 2018

Past Teaching

Digital Humanities Short Course

DHSI Summer Institute 2017 and 2018: Introduction to Machine Learning in the Digital Humanities

In June 2017 and June 2018 I co-taught a one week course in digital humanities with Paul Barrett at DHSI.

The class notes were developed using R Markdown and are available on Github and Rstudio Cloud.

Course Description: This course takes an introductory approach to machine learning in digital humanities topics. Participants will learn essential concepts in machine learning and use machine learning tools to collect and analyze literary, historical, and social media data sets using a number of machine learning approaches. The course will include an optional introduction to the R programming language; knowledge of this language will provide students with an opportunity to develop their own machine learning algorithms. In addition to the technical dimension of machine learning, we will also discuss the hermeneutic challenges posed by machine learning to the digital humanities, particularly as technical decisions enable specific ways of engaging in humanities scholarship: In what ways do DH scholars need to be cautious about the ‘results’ offered by machine learning algorithms, and what is the relationship between those results and humanities forms of knowledge?

Teaching Resources

Resouces for Data Science and Statistics Educators

Design of Scientific Studies

A third year cross-listed graduate course in the design of experiments and observational studies. Topics include: randomization, power and sample size, observational studies, and the propensity score.

Introduction to Data Science

A first year course in data science and statistical reasoning.

Introductory Statistics

An non-mathematical course in statistical concepts and reasoning.

Contact

  • nathan.taback@utoronto.ca
  • Sidney Smith Hall, 100 St. George Street, Toronto, Ontario, Canada, M5S 3G3
  • email for appointment

Recent Posts

Industry Day: Data Science and AI (Part of the Statistical Inference, Learning and Models in Data Science Conference)

Date: September 28, 2018

Location: MaRS Discovery District (101 College St, Toronto, ON)

Register for Industry Day here

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