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Excellence in Teaching

Excellence in Teaching
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United States , Ian Saldanha , Erin Pearson , Melinda Sawyer , Travis Rieder , Pamala Martin , John Ellis , Benjamin Lozare , Larissa Shimoda , Kathrin Herrmann , Kwaku Nuamah , Claire Twose , Franklin Adkinson , Rolf Klemm , Mahendra Damarla , Megan Singleton , Lori Rosman , Jennifer Dodson , Ira Gooding , Sarah Poynton , Dale Lupu , Daniel Obeng , Sean Evans , Julie Cady , Sarah Dalglish , Julie Nanavati ,

Christina J. Weiland

Christina Weiland is an associate professor at the School of Education at the University of Michigan, where she is affiliated with the Educational Studies department, the Combined Program in Psychology and Education program, and the Education Policy Initiative at the Ford School of Public Policy. She also serves as core faculty for the University of Michigan’s Predoctoral Training Program and Postdoctoral Training Program in Causal Inference in Education Policy Research.
Experience ....

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Statistics, Machine Learning, Causal Inference: What's the difference anyways?

Statistics, Machine Learning, Causal Inference, …
People coming from these different modeling cultures often have misunderstandings, and fight over whether logistic regression is “AI” or statistics.
Why the big differences?
1/n
2/n The tools and methods have quite a big overlap. Linear regression models are used in stats, ml and causality. ML draws heavily from statistical models. So, misunderstandings and differences can’t be solely explained by the tools that are used.
But what is it then?
3/n The difference is subtle, but fundamental.
Each modeling culture has its own definition of what constitutes a good model.
Let’s have a look at these different “generalization mindsets”. ....

Machine Learning , Causal Inference , இயந்திரம் கற்றல் ,

Toward Clearer Questions in Mendelian Randomization Studies

Toward Clearer Questions in Mendelian Randomization Studies
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Dress code and rape culture | Political Economy

Dress code and rape culture | Political Economy
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