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Overview of Machine
Learning & Feature
Engineering
Machine Learning 101 Tutorial
Strata + Hadoop World, NYC, Sep 2015
Alice Zheng, Dato
1
2
About us
Chris DuBois
Intro to recommenders
Alice Zheng
Overview of ML
Piotr Teterwak
Intro to image search & deep learning
Krishna Sridhar
Deploying ML as a predictive service
Danny Bickson
TA
Alon Palombo
TA
3
Why machine learning?
Model data.
Make predictions.
Build intelligent
applications.
Classification
Predict amongst a discrete set of classes
4
5
Input Output
6
Spam filtering
data prediction
Spam
vs.
Not spam
Text classification
EDUCATION
FINANCE
TECHNOLOGY
Regression
Predict real/numeric values
8
9
Stock market
Input
Output
Similarity
Find things like this
10
11
Similar products
Product I’m buying
Output: other products I might be interested in
12
Given image, find similar images
http://www.tiltomo.com/
Recommender systems
Learn what I want before I know it
13
14
15
Playlist recommendations
Recommendations form
coherent & diverse sequence
16
Friend recommendations
Users and “items” are of
the same type
Clustering
Grouping similar items
17
18
Clustering images
Goldberger et al.
Set of Images
19
Clustering web search results
20
Machine learning … how?
Data
Answers
I fell in love the instant I laid
my eyes on that puppy. His
big eyes and playful tail, his
soft furry paws, …
Many systems
Many tools
Many teams
Lots of methods/jargon
21
The machine learning pipeline
I fell in love the instant I laid
my eyes on that puppy. His
big eyes and playful tail, his
soft furry paws, …
Raw data
Features
Models
Predictions
Deploy in
production
22
Three things to know about ML
• Feature = numeric representation of raw data
• Model = mathematical “summary” of features
• Making something that works = choose the right model
and features, given data and task
Feature = numeric representation of raw data
24
Representing natural text
It is a puppy and it is
extremely cute.
What’s important?
Phrases? Specific
words? Ordering?
Subject, object, verb?
Classify:
puppy or not?
Raw Text
{“it”:2,
“is”:2,
“a”:1,
“puppy”:1,
“and”:1,
“extremely”:1,
“cute”:1 }
Bag of Words
25
Representing natural text
It is a puppy and it is
extremely cute.
Classify:
puppy or not?
Raw Text Bag of Words
it 2
they 0
I 1
am 0
how 0
puppy 1
and 1
cat 0
aardvark 0
cute 1
extremely 1
… …
Sparse vector
representation
26
Representing images
Image source: “Recognizing and learning object categories,”
Li Fei-Fei, Rob Fergus, Anthony Torralba, ICCV 2005—2009.
Raw image:
millions of RGB triplets,
one for each pixel
Classify:
person or animal?
Raw Image Bag of Visual Words
27
Representing images
Classify:
person or animal?
Raw Image Deep learning features
3.29
-15
-5.24
48.3
1.36
47.1
-
1.92
36.5
2.83
95.4
-19
-89
5.09
37.8
Dense vector
representation
28
Feature space in machine learning
• Raw data  high dimensional vectors
• Collection of data points  point cloud in feature space
• Feature engineering = creating features of the appropriate
granularity for the task
Crudely speaking, mathematicians fall into two
categories: the algebraists, who find it easiest to reduce
all problems to sets of numbers and variables, and the
geometers, who understand the world through shapes.
-- Masha Gessen, “Perfect Rigor”
30
Algebra vs. Geometry
a
b
c
a2 + b2 = c2
Algebra Geometry
Pythagorean
Theorem
(Euclidean space)
31
Visualizing a sphere in 2D
x2 + y2 = 1
a
b
c
Pythagorean theorem:
a2 + b2 = c2
x
y
1
1
32
Visualizing a sphere in 3D
x2 + y2 + z2 = 1
x
y
z
1
1
1
33
Visualizing a sphere in 4D
x2 + y2 + z2 + t2 = 1
x
y
z
1
1
1
34
Why are we looking at spheres?
= =
= =
Poincaré Conjecture:
All physical objects without holes
is “equivalent” to a sphere.
35
The power of higher dimensions
• A sphere in 4D can model the birth and death process of
physical objects
• High dimensional features can model many things
Visualizing Feature Space
37
The challenge of high dimension geometry
• Feature space can have hundreds to millions of
dimensions
• In high dimensions, our geometric imagination is limited
- Algebra comes to our aid
38
Visualizing bag-of-words
puppy
cute
1
1
I have a puppy and
it is extremely cute
I have a puppy and
it is extremely cute
it 1
they 0
I 1
am 0
how 0
puppy 1
and 1
cat 0
aardvark 0
zebra 0
cute 1
extremely 1
… …
39
Visualizing bag-of-words
puppy
cute
1
1
1
extremely
I have a puppy and
it is extremely cute
I have an extremely
cute cat
I have a cute
puppy
40
Document point cloud
word 1
word 2
Model = mathematical “summary” of features
42
What is a summary?
• Data  point cloud in feature space
• Model = a geometric shape that best “fits” the point cloud
43
Clustering model
Feature 2
Feature 1
Group data points tightly
44
Classification model
Feature 2
Feature 1
Decide between two classes
45
Regression model
Target
Feature
Fit the target values
Visualizing Feature Engineering
47
When does bag-of-words fail?
puppy
cat
2
1
1
have
I have a puppy
I have a cat
I have a kitten
Task: find a surface that separates
documents about dogs vs. cats
Problem: the word “have” adds fluff
instead of information
I have a dog
and I have a pen
1
48
Improving on bag-of-words
• Idea: “normalize” word counts so that popular words
are discounted
• Term frequency (tf) = Number of times a terms
appears in a document
• Inverse document frequency of word (idf) =
• N = total number of documents
• Tf-idf count = tf x idf
49
From BOW to tf-idf
puppy
cat
2
1
1
have
I have a puppy
I have a cat
I have a kitten
idf(puppy) = log 4
idf(cat) = log 4
idf(have) = log 1 = 0
I have a dog
and I have a pen
1
50
From BOW to tf-idf
puppy
cat1
have
tfidf(puppy) = log 4
tfidf(cat) = log 4
tfidf(have) = 0
I have a dog
and I have a pen,
I have a kitten
1
log 4
log 4
I have a cat
I have a puppy
Decision surface
Tf-idf flattens
uninformative
dimensions in the
BOW point cloud
51
Entry points of feature engineering
• Start from data and task
- What’s the best text representation for classification?
• Start from modeling method
- What kind of features does k-means assume?
- What does linear regression assume about the data?
Dato’s Machine Learning Platform
53
Dato’s machine learning platform
Raw data
Features Models
Predictions
Deploy in
production
GraphLab Create
Dato Distributed
Dato Predictive Services
54
Data structures for feature engineering
Features SFrames
User Com.
Title Body
User Disc.
SGraphs
55
Machine learning toolkits in GraphLab Create
• Classification/regression
• Clustering
• Recommenders
• Deep learning
• Similarity search
• Data matching
• Sentiment analysis
• Churn prediction
• Frequent pattern mining
• And on…
Demo
57
Dimensionality reduction
Feature 1
Feature 2
Flatten non-useful features
PCA: Find most non-flat
linear subspace
58
PCA : Principal Component Analysis
Center data at origin
59
PCA : Principal Component Analysis
Find a line, such that
the average distance of
every data point to the
line is minimized.
This is the 1st Principal
Component
60
PCA : Principal Component Analysis
Find a 2nd line,
- at right angles to the 1st
- such that the average
distance of every data
point to the line is
minimized.
This is the 2nd Principal
Component
61
PCA : Principal Component Analysis
Find a 3rd line
- at right angles to the
previous lines
- such that the average
distance of every data
point to the line is
minimized.
…
There can only be as many
principle components as
the dimensionality of the
data.
Demo
63
Coursera Machine Learning Specialization
• Learn machine learning in depth
• Build and deploy intelligent applications
• Year long certification program
• Joint project between University of Washington + Dato
• Details:
https://www.coursera.org/specializations/machine-learning
64
Next up today
alicez@dato.com @RainyData, #StrataConf
11:30am - Intro to recommenders
Chris DuBois
1:30pm - Intro to image search & deep learning
Piotr Teterwak
3:30pm - Deploying ML as a predictive service
Krishna Sridhar

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