Introduction to Applied Machine Learning in R by Dr. Brian J. Spiering What is applied machine learning? Machine learning is programming computers to automatically learn and generalize from examples. It allows for predictions or decisions beyond explicit programming of rules. Applying machine learning is a force multiplier for analysts, given the rise in the volume and velocity of data. This talk will the cover the basic, practical applications of machine learning in common contexts using the R programming language. Presented at Bay Area Entrepreneurs in Statistics

1. Introduction to
Applied Machine Learning
in R
Dr. Brian J. Spiering

2. lorem
• lorem

3. Introduction
to
Applied
Machine Learning
in
R

4. Introduction

5. Applied (vs. Theoretical)

6. What I talk about
when I talk about
machine learning
(apologies to Raymond Carver & Birdman)

7. machine learning (ml):
Programming computers
to automatically learn and
generalize from examples

9. Software might eat the world,
but algorithms will digest it
-Bruno Aziza

10. Machine Learning
• lorem

12. ml process

13. Take a step back.
Look at the bigger picture.
- Frank Underwood

14. Analytics Workflow

15. 1. Ask the right question

16. 2. Find the right data

17. 3. Process the data

18. 4. Model the data

19. 5. Share the results

20. Analytics Workflow
1. Ask the right question
2. Find the right data
3. Process the data
4. Model the data
5. Share the results

21. ml =
representation + evaluation + optimization

22. Representation
We need to represent the data and model
in a way computers can understand.

23. Evaluation
Separate good models from bad models

24. Optimization
A method to search among models.
Efficiently search for effective models.

25. Paradigms in ml
• Supervised Learning
• Unsupervised Learning

26. Supervised Learning
Infer a function from labeled data

27. Unsupervised Learning
Find hidden structure in unlabeled data

28. Linear Regression

29. Linear Regression
Fit a line to the data
(supervised learning)

30. R code interlude

31. Pros & Cons
+ Easy to fit
+ Easy to interpret
- Life is nonlinear
- Limited to predicting numeric output

33. Decision Trees
A series of branches that divide the data
(supervised learning)
x

34. R code interlude

35. Pros & Cons
+ White box model
+ Can fit most types of data
- Fragile
- Overfitting
- Out-of-sample prediction

37. Clustering
Grouping data so that data
in the same group is more
similar to each other than
to data in the other groups
(unsupervised learning)

38. R code interlude

39. Pros & Cons
+ Powerful and common unsupervised model
+ Good for a quick insights into data
- Difficult to identify or define clusters
- Slow to fit

40. ROAP
(Random Acts of Pizza)

41. Suggested Resources:
general ml
1. Data Mining and Analysis
2. Machine Learning: A Probabilistic Perspective

42. Suggested Resources: R
1. Try R from Code School
2. R in a Nutshell
3. The Art of R Programming

43. Suggested Resources: ml in R
An Introduction to Statistical Learning:
with Applications in R
Book
Video & Slides
Applied Predictive Modeling

44. LiveCareer
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