This 3 day workshop is intended to empower participants to perform serious nonlinear statistical modeling with the R statistical software. I assume that you are familiar with linear regression and R.
- Polynomial regression (review)
- Regression Splines
- Smoothing Splines
- Generalized additive models
- Modeling time series with GAMs
- Resampling Methods
- Cross-validation
- Bootstrapping
- CART
- Loss Functions for trees
- Optional: Surrogate Variables
- Random Forests
- Variable Importance
The workshop will contain plenty of hands-on, interactive explorations of real data sets.
You should install the R language and its popular IDE RStudio prior.
When you start RStudio you should see 3 panels, one of them the Console where you can type commands.
#mandatory
if (!require(pacman)) install.packages("pacman")
library(pacman)
p_load(gamair, lubridate,knitr,dygraphs,xts,ISLR,splines,gam,boot,mgcv,ggplot2,scales,partykit, install = TRUE)I would decline the compilation from source.
Be prepared to wait a while, lots of dependent packages are being installed as well.
Berlin School of Economics and Law
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Main book
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https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
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Alternative books
- Data Science https://www.manning.com/books/practical-data-science-with-r Make Your Own Neural Network (Tariq Rashid) Statistics Andy Field, Jeremy Miles, Zoe Field (2012), Discovering Statistics Using R, SAGE https://uk.sagepub.com/en-gb/eur/discovering-statistics-using-r/book236067 https://www.openintro.org/stat/ http://onlinestatbook.com/
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Useful MOOCs
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Statistics
https://classroom.udacity.com/courses/st101 Intro to Descriptive Statistics: https://www.udacity.com/course/intro-to-descriptive-statistics-- ud827 Intro to Inferential Statistics: https://www.udacity.com/course/intro-to-inferential-statistics--ud201
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R Programming https://www.coursera.org/learn/r-programming/ Machine Learning https://www.coursera.org/learn/machine-learning
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Why R is still one of the best data science language to learn today
http://sharpsightlabs.com/blog/r-recommend-data-science/ https://stackoverflow.blog/2017/10/10/impressive-growth-r/ -
Ethical Issues in Machine Learning: https://www.propublica.org/series/machine-bias Fairness of algorithms Equality of Opportunity in Supervised Learning Photo Categorization http://www.wnyc.org/story/deep-problem-deep-learning/ https://www.forbes.com/sites/mzhang/2015/07/01/google-photos-tags-two-african-americans-as-gorillas-through-facial-recognition-software/