This document provides an introduction to machine learning, including definitions, types of learning (supervised, unsupervised, reinforced), and typical processes. It discusses issues like underfitting and overfitting. It also introduces Spark MLLIB, an Apache Spark library for machine learning that contains parallel algorithms. Key algorithms in MLLIB mentioned include k-means clustering, random forests, and principal component analysis (PCA).

1. Machine Learning – 101
Behzad Altaf

2. Coursera – Andrew NG Machine Learning
https://www.coursera.org/learn/machine-learning/home/welcome

3. At a basic level, machine learning is about predicting the future based on
the past. [Hal Daumé III]
Systems that automatically learn programs from data. [Domingos]
Teaching a computer about the world. [Mark Dredze]

4. ML Application

5. Kinds of Learning
 Supervised Learning
 Unsupervised Learning
 Semi-supervised Learning
 Reinforced Learning
https://en.wikipedia.org/wiki/Machine_learning

6. • Supervised Learning
Supervised learning is the machine learning task of inferring a function (f) from labeled training data.
- We have data with already attached labels (e.g. we know spam/non spam difference)
- We predict a pattern that fits the data
- We apply this pattern to new data and predict
Goal: from the database (learning sample), find a function f of the inputs that approximates at best the output
 Symbolic output ⇒ classification problem
 Numerical output ⇒ regression problem
Supervised Learning

7. • Unsupervised Learning
In machine learning, the problem of unsupervised learning is that of trying to find
hidden structure in unlabeled data.
- We have some (a lot) data that we cannot make much sense of
- We use different algorithms to see if a pattern emerges
Unsupervised Learning

8. Supervised Learning

9. Unsupervised Learning

10. Typical Process
Data
Training
Test
Hypothesis
Validate

11. Typical Process with Cross Validation
Data
Training
Test
Hypothesis
Cross
Validation
Tune/
Select Best
Validate

12. Issues in Machine Learning
Under fitting

13. Over Fitting
Issues in Machine Learning

14. Loan Type
(x1)
Loan Amount
(x2)
Income Range
(x3)
Car Segment
(x4)
Customer Type
y
House 50,000 > 50,000 Audi HNI
NA 10,000 < 10,000 NA LNI
y = ⱷ0+ ⱷ1.x1 + ⱷ2.x2 + ⱷ3.x3 + ⱷ4.x4
Data
Loan Type
(x1)
Loan Amount
(x2)
Income Range
(x3)
Car Segment
(x4)
Customer Type
y
1 50,000 5 5 HNI
0 10,000 1 0 LNI
1 20,000 3 2 ?
Feature
Vector
1-Dimensional
Array

15. What if the DATA IS

16. The curious case of

17. Spark MLLIB
http://spark.apache.org/

18. Spark and Machine Learning
• MLlib is Spark’s library of machine learning functions.
• It contains only parallel algorithms that run well on clusters.
• All learning algorithms require defining a set of features for each item, which will be fed
into the learning function.
• An important step is feature extraction and transformation to produce these feature
vectors.

19. http://spark.apache.org/mllib/
Algorithms in MLLIB

20. K-Means

21. Random Forest
http://opinions5.blogspot.in/2013/08/random-forest-confidence.html

22. Random Forest – tree a
http://www.slideshare.net/InfoQ/a-taste-of-random-decision-forests-on-apache-spark

23. Random Forest – tree b
http://www.slideshare.net/InfoQ/a-taste-of-random-decision-forests-on-apache-spark

24. PCA
http://www.scipy-lectures.org/packages/scikit-learn/#dimension-reduction-with-principal-component-analysis