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Machine Learning in a
Flash
Kory Becker
September, 2017, http://primaryobjects.com
1
Sponsored by
2
AI IS GOOD
AI !== Machine Learning
 Logical AI, Symbolic, Knowledge-
based
 Pattern Recognition, Representation
 Inference, Common Sense, Planning
 Heuristics, Ontology, Artificial Life,
Genetic
 Machine Learning, Statistics
3
Machine Learning
Algorithms
Supervised
Linear Regression
Logistic Regression
Support Vector Machines
Neural Networks
Unsupervised
K-means Clustering
Principal Component Analysis (Dimensionality
Reduction)
4
Linear Regression
Logistic Regression
Logistic Regression
Linear Classification
Support Vector Machine
Non-Linear Classification
Support Vector Machine
Gaussian Kernel
Neural Network
Modeled after the human brain
Input, hidden, output layers
Supervised Learning
Training data
Backpropagation, gradient descent
Unsupervised Learning
Deep learning
https://goo.gl/dHMTK1
Neural Network
yes/no
a
b
c
Binary Classification
Perceptron
a
b
∑ 0 or 1
w1
w2
f
Binary Classification
Perceptron
0
1
∑
0.2
0.8
f
𝑓 𝑥 =
1
1 + 𝑒−𝑥
≥ 0.5
= (0 ∗ 0.2) + (1 ∗ 0.8)
𝑓 𝑥 = 𝑥 > 0
0 or 1
https://goo.gl/CwMNzQ
Binary Classification
𝑜𝑟
Training Data
# legs horn weight class
0 1 0.82 narwhal
0 1 0.70 narwhal
0 1 0.90 narwhal
4 0 0.40 horse
4 0 0.50 horse
4 1 0.60 unicorn
4 1 0.50 unicorn
Horse, Unicorn, Narwhal?
Horse, Unicorn, Narwhal?
# legs horse
Multinomial
horn
weight
unicorn
narwhal
0.34
0.88
0.110
1
0.82
Horse, Unicorn, Narwhal?
Multinomial
Image Recognition
Neural Networks
> predict(fit, image, type='prob')
T-shirt/top 0.07942545
Trouser 0.6527636
Pullover 0.03322074
Dress 0.05978968
Coat 0.05326422
Sandal 0.0231558
Shirt 0.06682828
Sneaker 0.00359941
Bag 0.01912605
Ankle boot 0.008826793
28x28 Pixel Image
Image Recognition
784
Inputs
Image Recognition
10 Output
Classes
784
Inputs
28x28 Pixel Image
Image Recognition
0.07942545
0.6527636
0.03322074
0.05978968
0.05326422
0.0231558
0.06682828
0.00359941
0.01912605
0.008826793
784
Inputs
28x28 Pixel Image
Image Recognition
T-shirt/top
Trouser
Pullover
Dress
Coat
Sandal
Shirt
Sneaker
Bag
Ankle boot
784
Inputs
28x28 Pixel Image
Pop Quiz!
Question 1: Supervised
or Unsupervised?
 You are designing an agent for The Matrix.
 It’s task is to classify people that are threats to the system.
 Feature Set:
 Age
 IQ
 Level of Education
 # of Times They Watched the Movie The Matrix
 Training Set of 100,000 people: 50k threats, 50k non-threats
Question 2: Supervised
or Unsupervised?
 You are designing the brain of a battle robot.
 It’s primary attack is hand-to-hand combat. Your task is to
find the most effective move combos.
 Feature Set:
 # of Kicks
 # of Punches
 # of Head-butts
 # of Leg Sweeps
 Training Set of 100,000 winning battles
Natural Language
Processing
Convert text into a numerical representation
Find commonalities within data
Clustering
Make predictions from data
Classification
Category, Popularity, Sentiment,
Relationships
Bag of Words Model
Corpus
Cats like to chase mice.
Dogs like to eat big bones.
Create a Dictionary Dictionary
0 - cats
1 - like
2 - chase
3 - mice
4 - dogs
5 - eat
6 - big
7 - bones
Cats like to chase mice.
Dogs like to eat big bones.
Corpus
Digitize Text
Cats like to chase mice.
1 1 1 1 0 0 0 0
Dogs like to eat big bones.
0 1 0 0 1 1 1 1
Vector Length = 8
Corpus
Dictionary
0 - cats
1 - like
2 - chase
3 - mice
4 - dogs
5 - eat
6 - big
7 - bones
Classify Documents
(eating)
Cats like to chase mice.
1 1 1 1 0 0 0 0
Dogs like to eat big bones.
0 1 0 0 1 1 1 1
0
1
Corpus
Dictionary
0 - cats
1 - like
2 - chase
3 - mice
4 - dogs
5 - eat
6 - big
7 - bones
Predict on New Data
Cats like to chase mice.
1 1 1 1 0 0 0 0
Dogs like to eat big bones.
0 1 0 0 1 1 1 1
Bats eat bugs.
0 0 0 0 0 1 0 0
0
1
?
Dictionary
0 - cats
1 - like
2 - chase
3 - mice
4 - dogs
5 - eat
6 - big
7 - bones
Predict on New Data
Cats like to chase mice.
1 1 1 1 0 0 0 0
Dogs like to eat big bones.
0 1 0 0 1 1 1 1
Bats eat bugs.
0 0 0 0 0 1 0 0
0
1
?
Dictionary
0 - cats
1 - like
2 - chase
3 - mice
4 - dogs
5 - eat
6 - big
7 - bones
Predict on New Data
Cats like to chase mice.
1 1 1 1 0 0 0 0
Dogs like to eat big bones.
0 1 0 0 1 1 1 1
Bats eat bugs.
0 0 0 0 0 1 0 0
0
1
1
Dictionary
0 - cats
1 - like
2 - chase
3 - mice
4 - dogs
5 - eat
6 - big
7 - bones
Does it Really Work?
> data
[1] "Cats like to chase mice." "Dogs like to eat big
bones."
> train
big bone cat chase dog eat like mice y
1 0 0 1 1 0 0 1 1 0
2 1 1 0 0 1 1 1 0 1
> predict(fit, newdata = train)
[1] 0 1
> data2
[1] "Bats eat bugs."
> test
big bone cat chase dog eat like mice
1 0 0 0 0 0 1 0 0
> predict(fit, newdata = test)
[1] 1
Document
Term Matrix
100% Accuracy Training
Test Case
Success! Source code:
https://goo.gl/UxjPBs
Unsupervised Learning
Finding patterns in data
Grouping similar data into clusters
Does not require labeled data
Exploratory data analysis
Predict clusters for new data!
K-Means Clustering
Popular clustering algorithm
Groups data into k clusters
Data points belong to the cluster with closest mean
Each cluster has a centroid (center)
k-Means Algorithm
Choose a value for k (number of clusters)
 Guess
 Rule of thumb: ~~(Math.sqrt(points.length * 0.5))
Initialize centroids
 Random
 Farthest Point
 K-means++
Assign data points to closest centroid
Move centroids to center of assigned points
Demo: https://goo.gl/AjNEJk
Clustering Example 1
Clustering Example 1
Clustering Example 1
Clustering Example 2
Clustering Example 2
Predicting Color Groups
rgb(255, 0, 0)
rgb(0, 255, 0)
rgb(0, 0, 255)
rgb(200, 0, 150)
rgb(50, 199, 135)
rgb(100, 180, 255)
red
green
blue
?
?
?
Predicting Color Groups
rgb(255, 0, 0)
rgb(0, 255, 0)
rgb(0, 0, 255)
rgb(200, 0, 150)
rgb(50, 199, 135)
rgb(100, 180, 255)
red
green
blue
?
?
?
= 16777216
Encoding
= 65280
= 13107350
= 3327879
= 6599935
= 255
(Red * 256 * 256) + (Green * 256) + (Blue)
1000 Colors
100 Colors
Calculating Centroids
Classifying Colors to a Cluster
Grouping Colors into their Cluster
Predicting Color Groups
rgb(241, 52, 11)
rgb(80, 187, 139)
rgb(34, 15, 194)
?
?
?
Predicting on New Data
Predicting on New Data
Categorizing Stocks & Bonds
Data Source: Vanguard ETF funds
Data Fields:
 Ticker, Asset Class, Expense Ratio
 Price, Change 1, Change 2, SEC Yield
 YTD, Year 1, Year 5, Year 10
 Since Inception
Can We Predict a Category?
Categorizing Stocks & Bonds
International
Stocks
Interm Bond
Long
Bond
Asset Classes
Stock Sector
Stock Mid-Cap Blend
Stock Large-Cap Value
International
Bond Inter-term Investment
Bond Inter-term Government
Bond Long-term Government
Categorizing Stocks & Bonds
Asset Classes
Stock Sector
Stock Mid-Cap Blend
Stock Large-Cap Value
International
Bond Inter-term Investment
Bond Inter-term Government
Bond Long-term Government
Categorizing Stocks & Bonds
Features
Ticker, Asset Class, Expense Ratio
Price, Change 1, Change 2, SEC Yield
YTD, Year 1, Year 5, Year 10
Since Inception
Categorizing Stocks & Bonds
Example Data
VYM, Stock - Large-Cap Value, 0.08%, $77.95, -
$0.11, -0.14%, 3.09%B, 4.49%, 12.73%, 13.64%,
7.08%, 7.53% (11/10/2006)
VIG, Stock - Large-Cap Blend, 0.08%, $92.39, -
$0.21, -0.23%, 1.93%B, 9.62%, 13.75%, 12.75%,
7.41%, 7.92% (04/21/2006)
Categorizing Stocks & Bonds
Which of these look similar?
Categorizing Stocks & Bonds
► VYM, Stock - Large-Cap Value
► 4.49, 12.73, 13.64, 7.08
► VIG, Stock - Large-Cap Blend
► 9.62, 13.75, 12.75, 7.41
► EDV, Bond - Long-term
► 6.48, -10.37, 3.38, 0
► VCIT, Bond - Inter-term
► 3.47, 1.1, 4.04, 0
Group Into Five Clusters
1. Stock
2. StockBigGain
3. International
4. SmallMidLargeCap
5. Bond
Categorizing Stocks & Bonds
Why?
55
2
= 5
Predicting on New Data
Use centroids from training
Determine cluster for each test point
Assign label
Easy!
Categorizing Stocks & Bonds
Predicting on New Data
Categorizing Stocks & Bonds
► VYM, Stock - Large-Cap Value
► 4.49, 12.73, 13.64, 7.08
► EDV, Bond - Long-term
► 6.48, -10.37, 3.38, 0
► VTI, Stock – Large-Cap Blend
► 9.04, 18.49, 14.55, 7.39
Stock
Bond
?
Predicting on New Data
Categorizing Stocks & Bonds
► VYM, Stock - Large-Cap Value
► 4.49, 12.73, 13.64, 7.08
► EDV, Bond - Long-term
► 6.48, -10.37, 3.38, 0
► VTI, Stock – Large-Cap Blend
► 9.04, 18.49, 14.55, 7.39
Stock
Bond
?
Results – Did It Work?
►VEU, International, 1, International
►VNQI, International, 1,International
►VXUS, International, 1, International
►BLV, Bond - Long-term, 3, Stock
►BIV, Bond - Inter-term, 4, Bond
►VCLT, Bond - Long-term, 4, Bond
►BSV, Bond - Short-term, 4, Bond
 VIG, Stock - Large-Cap Blend, 5, SmallMidLargeCap
 VUG, Stock - Large-Cap Growth, 5, SmallMidLargeCap
 VTI, Stock - Large-Cap Blend, 5, SmallMidLargeCap
Categorizing Stocks & Bonds
Not Bad! 
Thank you!
Kory Becker
http://primaryobjects.com
@primaryobjects

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