Machine Learning: Theory and Applications

Length: Five Days
Instructor: Doug Steinley
Software Demonstrations: Python, R
Lifetime Access: No expirations

Student: $990
Professional: $1290

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Category: Asynchronous, Workshops

Machine Learning is a five-day workshop focused on the application and interpretation of both traditional and next-generation machine learning (also statistical learning) approaches. One of the fastest growing areas of statistics and data analysis, machine learning applications have increased rapidly within the psychological, health, and social sciences. These techniques are applied with the goal of producing robust models for predictions (continuous outcomes) or classifications (categorical outcomes), and focus on achieving high accuracy rather than testing null hypothesis or statistical significance. This workshop provides participants with the understanding and practical tools to choose between machine learning techniques and use these with confidence.

The workshop begins with common, fundamental models that are widely used, such as regression and logistic regression, and uses these to introduce and understand more advanced approaches such as regularization, splines, support vector machines, classification trees, and discriminant analysis.  Along the way, a comprehensive approach to assessing model fit and protecting against overfitting will be developed.  Finally, this workshop will also delve into the tension between “prediction” and “inference” and explores the implications for common research applications.

Instructor

Doug Steinley, Ph.D.

Doug Steinley is a Professor in the Department of Psychology at the University of Missouri. His research and teaching focus on multivariate statistical methodology, with a primary interest in cluster analysis (both traditional procedures and more modern mixture modeling techniques) and social network analysis. Read More

Workshop Details

Reviews

Doug was a very engaging and easy to understand teacher. I loved his analogies and consistent themes of important grounding concepts (e.g. bias vs. variance).

I just liked Doug's attitude and excitement in covering the topics he did. It makes you want to learn more 🙂

Doug was very knowledgeable about the material. He was very open to questions and let the students direct what topics where talked about more.

I really liked that you recorded the lectures. That was helpful. Also, I think your class really translated to zoom well. Also, this sounds silly, but you're really good at using features of zoom like drawing on the slides and that was super helpful.

Doug was very energetic and very knowledgeable about the subject matter. Doug has a warm and charismatic personality, and even though statistics aren't my strong suit, I enjoyed this course! He made the material much more engaging, compelling, and interesting than I thought possible. Also, he was very good at addressing student questions and being very open with communication throughout the class.

Doug was clear with his examples and was clearly knowledgeable on the topic. This class is theory-heavy, but he explained concepts and theoretical approaches with depth and clarity and was always open to additional questions. He clearly put effort into building students' knowledge and provided an open and non-judgmental environment for us.

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