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Presentation to the Northwestern University Department of Psychology

Whereas the classic statistical methods (i.e., inferential models) traditionally used in the social and behavioral sciences emphasize interpretability and statistical significance, machine learning methods (i.e., predictive models) emphasize complexity and prediction accuracy. Machine learning methods are thus particularly well-suited for applications where (1) there are nonlinear and complex relationships among many predictor variables and (2) accurately predicting the outcome variable is more important than fully understanding the relationships between variables. This one-hour talk will provide a conceptual introduction to the topic of machine learning (specifically supervised predictive modeling) aimed at providing the audience with an understanding of the relevant concepts and intuitions behind the typical machine learning workflow. Examples will emphasize applications in psychology/psychiatry.

2 Responses to “A Conceptual Introduction to Machine Learning”

  1. Yoshiaki Fujita

    Dear Dr. Girard,

    This is Yoshiaki Fujita, a former your Psyc469 student.

    Now, I am studying Machine Learning so your slide is very helpful for me.

    Here, you say, “This one-hour talk.” Does this mean you post a video to explain the slide? Or the slide is your one-hour talk? Or do you mean that your coming five days workshop is the one-hour talk? (https://www.pittmethods.com/applied-ml)

    I am interested in the one-hour talk about Machine Learning, so if you give an answer, it is helpful.

    p.s. Thanks to your last August post, I succeed in setting Ubuntu on my Windows PC, thanks.

    • Jeffrey Girard

      Hi Yoshi, these are the slides from a one-hour talk I gave for Northwestern University last week. I’m not sure if it was video recorded; I’ll look into that.

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