generalized additive models 2

Description

see attachedSources for r markdown presentationshttps://bookdown.org/yihui/rmarkdown/presentations.htmlhttps://www.r-bloggers.com/2019/09/mastering-r-presentations/https://arm.rbind.io/slides/xaringan.html#1https://pkg.garrickadenbuie.com/xaringanthemer/index.htmlhttp://svmiller.com/blog/2019/08/r-markdown-template-beamer-presentations/You are writing a lecture to teach a Statistical/Machine Learning topic to your
classmates.
YOUR TOPIC: Generalized Additive Models (GAMs)
Intro slide on GAMs
You must clearly explain the important concepts related to your topic, a bit of
the lighter theory is expected within reason and/or explanation of the
algorithm. This is not meant to be an extremely long or thorough lecture, so
find a reasonable balance! Don’t copy directly from datacamp or other sources
(should be at least 6 slides)
o You can reference other resources, please include a bibliography slide with
your resources at the end
You should motivate when AND why we would use a particular method. (at least
2-3 slides)
o This method should be well motivated, interesting, insightful, and novel. It’s
clear when and why we would use the particular method.
You are required to use RMarkdown to write your presentation. You can make
slides that are .pdf files or html files. Slides that are HTML files must be selfcontained into a single .html file.




Your slides should be as professional looking as possible.


All R code should be important and immediately useful for teaching the material.
We should be able to reproduce your results (some randomness is
understandable, but setting a seed minimizes this) by running your code for
ourselves. (You will likely not need any R code for this intro part unless it is super
brief)
You should not dwell on trivial things. This would be fluff to make your
presentation unnecessarily long and doesn’t add value. This includes needless
analysis, code, tables, and plots.
o Example: Checking the assumptions in regression. It is well understood
that you checked the assumptions that justify the use of linear regression
and everything should be okay or else you would not be proceeding. It’s
not interesting nor useful to show these kinds of checks. However,
suppose the assumptions were violated and you had to do a
transformation, then you can briefly mention that a transformation was
done. If the issues are particularly interesting, you can comment a bit
further or show a brief plot or two, but the fact remains that you still
haven’t demonstrated the new concepts that you are trying to teach.
*You will NOT have to demonstrate your method
Only need to fulfill the first two rows (Introduction to GAM Concepts and Motivation and
Presentation of Theory)
Results and Code Review is not relevant for this part

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