1.
{
Introduction to
Machine Learning
Ryo Onozuka
2016/11/30

2.
 It is becoming popular that using machine
learning in business models.
 Understanding what machine learning does
inside it is needed to analyze those business
models.
Why Machine Learning?
2012 20162014
Big Data
Machine Learning
Data Analysis
Data from Google Trends

3.
What is Machine Learning?
 They are algorithm that is becoming wiser by
incorporating experience (i.e. data).
 They get knowledge and rules automatically
from data.
 They emerged when past researches on AI were
faced with the limit that human explicitly
supplied AI with knowledge and rules.
Training Data
Classification Model
Estimate
Boundary

4.
Rule Based Approach:
Before Machine Learning
 How can we classify people in this campus into some categories.
 Make a rule of the classification by human.
 When the amount of data are small or data is difficult to quantify,
the result from human’s inference is better. Machine cannot
answer what it didn’t know.
Looks young?
Looks tired? Teacher
Undergraduate
students
Graduate
students
Not young
Category
Condition
Young
Not tired Tried

5.
How Machine Learning Solve this?
 There are some approaches.
 Supervised Learning
 Regression, Naïve Bayes, k-NN, Neural Network, etc.
 Unsupervised Learning
 k-means
 Semi-supervised Learning
 Reinforcement Learning
Important!
 Main purpose
 Classification, Prediction, Recommendation

6.
How Supervised Learning Solve
this?
 First, we need LARGE amount of appropriate data on people in this
campus.
 Second, we need to extract FEATURES from each person.
 Third, we select one algorithm and train it.
Label
Undergrad.
Undergrad.
Teacher
Undergrad.
↓Training data for supervised learning
Id Young Hair Fashion Tired
1 young blond t shirts not tired
2 young black suits tired
3 not
young
white suits tired
4 young black t shirts not tired

7.
 It is like polls.
How do it learn a rule to
classify?
Feature Undergrad. Teacher
Tired +1 +1
Not tired +2
Young +3
Not young +1
Blond hair +1
Id Young Hair Fashion Tired
1 young blond t shirts not tired
2 young black suits tired
3 not
young
white suits tired
4 young black t shirts not tired
Label
Undergrad.
Undergrad.
Teacher
Undergrad.
Feature Undergrad. Teacher
Black hair +1
White hair +1
suits +1 +1
t shirts +2

8.
 How will it classify this person?
When a new person come up
Feature Undergrad. Teacher
Tired +1 +1
Not tired +2
Young +3
Not young +1
Blond hair +1
Feature Undergrad. Teacher
Black hair +1
White hair +1
suits +1 +1
t shirts +2
Tired, young, black hair, suits
Undergrad. Teacher
6 points 2 points
Undergrad. !!

9.
 1) How will it classify this person?
 2) Do you have any idea to improve its
accuracy?
 Change features that it extracts from people?
 Change a way to gather data for training?
 Change algorithm of it?
Question (10 min.)
Not tired, not young, black hair, suits

10.
How Unsupervised Learning Solve
this?
Id Young Hair Fashion Tired
1 young bold t shirts not tired
2 young black suits tired
3 not
young
white suits tired
4 young black t shirts not tired
Label
Undergrad.
Undergrad.
Teacher
Undergrad.
↓Training data for unsupervised learning
Unsupervised learning
don’t need labels
Id young
not
young
bold black white
t
shirts
suits tired
not
tired
1 1 1 1 1
2 1 1 1 1
3 1 1 1 1
4 1 1 1 1

11.
Make a distance matrix
Id young
not
young
bold black white
t
shirts
suits tired
not
tired
1 1 1 1 1
2 1 1 1 1
3 1 1 1 1
4 1 1 1 1
Calculate distance between every pair.
There are many types of distance.
1) Euclid distance
2) Correlation coefficient
3) Cosine similarity, etc.

12.
Calculate Euclid Distance
Id young
not
young
bold black white
t
shirts
suits tired
not
tired
Distan
ce
1 1 1 1 1
2 1 1 1 1
(1-1)2 (0-0)2
(1-0)2 (0-1)2 (0-0)2 (1-0)2 (0-1)2 (0-1)2 (1-0)2
√6
1 1 1 1 1
3 1 1 1 1
(1-0)2 (0-1)2 (1-0)2 (0-0)2 (0-1)2 (1-0)2 (0-1)2 (0-1)2 (1-0)2
2√2
1 1 1 1 1
4 1 1 1 1
(1-1)2 (0-0)2 (1-0)2 (0-1)2 (0-0)2 (1-1)2 (0-0)2 (0-0)2 (1-1)2
√2
2 1 1 1 1
3 1 1 1 1
(1-0)2 (0-1)2 (0-0)2 (1-0)2 (0-1)2 (0-0)2 (1-1)2 (1-1)2 (0-0)2
2
3 1 1 1 1

13.
Find the most similar people
1 2 3 4
1 1 √6 2√2 √2
2 √6 1 2 2
3 2√2 2 1 2√2
4 √2 2 2√2 1
↓ Distance Matrix
Id Young Hair Fashion Tired
1 young bold t shirts not tired
2 young black suits tired
3 not
young
white suits tired
4 young black t shirts not tired
↓ Gathered Data
The nearest!
The nearest!
2nd nearest
2nd nearest

14.
1 2 3 4
1 1 √6 2√2 √2
2 √6 1 2 2
3 2√2 2 1 2√2
4 √2 2 2√2 1
Show the Result in a form of
Dendrogram
↓ Distance Matrix
Id Young Hair
Fashio
n
Tired
1 young bold t shirts
not
tired
2 young black suits tired
3
not
young
white suits tired
not
↓ Gathered Data
1 4 2 3
Distance
3
2
0
Cluster A Cluster B
Use furthest one
as distance
between clusters

15.
Name clusters
Id Young Hair
Fashio
n
Tired
1 young bold t shirts
not
tired
2 young black suits tired
3
not
young
white suits tired
4 young black t shirts
not
tired
↓ Gathered Data
1 4 2 3
Distance
-1
0
1
Cluster A Cluster B
1 4
Cluster A
2 3
Cluster B
Not tired young
cluster?
Tired suits cluster?
→Are there better names?

16.
 Make a new distance matrix and a new
dendrogram
 Name each cluster
 What cluster will this person belong to?
 Do you have any idea to improve this result?
Question (15 min.)
Not tired, not young, black hair, suits

17.
 You are a data analyst of smart phone game company.
You want to predict whether new customer will buy
premium membership or not.
 1) Which algorithm do you use?
 2) How do you gather data?
 3) If you use supervised learning, how to make label for
training?
 4) What features will you extract from user activities?
Discussion (20 min.)