This document provides an overview of machine learning algorithms and techniques. It begins by distinguishing machine learning from other types of artificial intelligence. It then describes popular machine learning algorithms including supervised learning algorithms like linear regression, logistic regression, support vector machines, and neural networks as well as unsupervised learning algorithms like k-means clustering and principal component analysis. The document provides examples of how these algorithms can be applied to tasks like text classification, sentiment analysis, and clustering financial data into categories. It emphasizes that machine learning involves finding patterns in data to make predictions without being explicitly programmed.

1. Machine Learning in a
Flash
Kory Becker
August, 2017, http://primaryobjects.com
1
Sponsored by

2. 2
AI IS GOOD

3. AI !== Machine Learning
 Logical AI, Symbolic, Knowledge-
based
 Pattern Recognition, Representation
 Inference, Common Sense, Planning
 Heuristics, Ontology, Artificial Life,
Genetic
 Machine Learning, Statistics
3

4. Machine Learning
Algorithms
Supervised
Linear Regression
Logistic Regression
Support Vector Machines
Neural Networks
Unsupervised
K-means Clustering
Principal Component Analysis (Dimensionality
Reduction)
4

5. Linear Regression

6. Logistic Regression

7. Logistic Regression
Linear Classification

8. Support Vector Machine
Non-Linear Classification

9. Support Vector Machine
Gaussian Kernel

10. Pop Quiz!

11. 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

12. 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

13. Natural Language
Processing
Convert text into a numerical representation
Find commonalities within data
Clustering
Make predictions from data
Classification
Category, Popularity, Sentiment,
Relationships

14. Bag of Words Model
Corpus
Cats like to chase mice.
Dogs like to eat big bones.

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. Unsupervised Learning
Finding patterns in data
Grouping similar data into clusters
Does not require labeled data
Exploratory data analysis
Predict clusters for new data!

23. 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)

24. 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

25. Clustering Example 1

26. Clustering Example 1

27. Clustering Example 1

28. Clustering Example 2

29. Clustering Example 2

30. 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
?
?
?

31. 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)

32. 1000 Colors

33. 100 Colors

34. Calculating Centroids

35. Classifying Colors to a Cluster

36. Grouping Colors into their Cluster

37. Predicting Color Groups
rgb(241, 52, 11)
rgb(80, 187, 139)
rgb(34, 15, 194)
?
?
?
Predicting on New Data

38. Predicting on New Data

39. 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

40. Can We Predict a Category?
Categorizing Stocks & Bonds
International
Stocks
Interm Bond
Long
Bond

41. 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

42. 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

43. 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

44. 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

45. 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

46. Group Into Five Clusters
1. Stock
2. StockBigGain
3. International
4. SmallMidLargeCap
5. Bond
Categorizing Stocks & Bonds
Why?
55
2
= 5

47. Predicting on New Data
Use centroids from training
Determine cluster for each test point
Assign label
Easy!
Categorizing Stocks & Bonds

48. 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
?

49. 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
?

50. 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! 

51. Thank you!
Kory Becker
http://primaryobjects.com
@primaryobjects