An introduction to several techniques of machine learning.

1. Machine
Learning
Andrea Iacono
https://github.com/andreaiacono/MachineLearning

2. Machine Learning: Intro
What is Machine Learning?
[Wikipedia]: a branch of artificial intelligence
that allows the construction and the study of
systems that can learn from data

3. Machine Learning: Intro
Some approaches:
- Regression analysis
- Similarity and metric learning
- Decision tree learning
- Association rule learning
- Artificial neural networks
- Genetic programming
- Support vector machines
(classification and regression analysis)
- Clustering
- Bayesian networks

4. Machine Learning: Intro
Supervised learning
vs
Unsupervised learning
Machine learning
vs
Data mining

5. Machine Learning: Regression analysis
Regression Analysis
A statistical technique for estimating
the relationships among a dependent
variable and independent variables

6. Machine Learning: Regression analysis
Prediction of house prices
Size (x)
Price (y)
0.80
70
0.90
83
1.00
74
1.10
93
1.40
89
1.40
58
1.50
85
1.60
114
1.80
95
2.00
100
2.40
138
2.50
111
2.70
124
3.20
172
3.50
172

7. Machine Learning: Regression analysis
Prediction of house prices
Hypothesis:
h θ ( x )=θ0 + θ1 x

8. Machine Learning: Regression analysis
Prediction of house prices
Hypothesis:
h θ (x )=θ0 + θ1 x
Cost function for linear regression:
m
1
J (θ 0, θ1 )=
(h θ (x (i) )− y(i ) )2
∑
2m i=1

9. Machine Learning: Regression analysis
Prediction of house prices
Hypothesis:
h θ (x )=θ0 + θ1 x
Cost function for linear regression:
m
1
J (θ 0, θ1 )=
(h θ (x (i) )− y(i ) )2
∑
2m i=1
Gradient Descent
repeat until convergence :
m
1
(i )
(i )
θ 0=θ 0−α ∑ (hθ ( x )− y )
m i =1
m
1
θ1 =θ1 −α ∑ [(h θ (x (i) )− y(i )) x (i) ]
m i =1

10. Machine Learning: Regression analysis
Prediction of house prices
Iterative minimization of cost function
with gradient descent

11. Machine Learning: Regression analysis
Hands on

12. Machine Learning: Regression analysis
Regression analysis
- one / multiple variables
- linear / higher order curves
- several optimization algorithms
- linear regression
- logistic regression
- simulated annealing
- ...

13. Machine Learning: Regression analysis
Overfitting vs underfitting

14. Machine Learning: Similarity and metric learning
Similarity and metric learning
- concept of distance

15. Machine Learning: Similarity and metric learning
Euclidean distance
euclidean distance (p , q )=
√
n
∑ (p i −q i )2
i =1

16. Machine Learning: Similarity and metric learning
Manhattan distance
n
manhattan distance (p , q )=∑ ∣(p i −q i )∣
i =1

17. Machine Learning: Similarity and metric learning
Pearson's correlation
n
n
∑ pi ∑ qi
n
∑ (p i q i )− i =1
Pearson ' s correlation ( p , q )=
i =1
√
n
n
2
i
(∑ p −
i =1
i =1
n
n
2
(∑ p i )
i =1
n
2
n
(∑ qi )
i =1
n
)( ∑ q 2 −
i
i =1
)

18. Machine Learning: Similarity and metric learning
Collaborative filtering
Searches a large group of users for finding a
small subset that have tastes like yours.
Based on what this subset likes or dislikes
the system can recommend you other items.
Two main approaches:
- User based filtering
- Item based filtering

19. Machine Learning: Similarity and metric learning
User based filtering
- based on ratings given to
the items, we can measure
the distance among users
- we can recommend to the
user the items that have
the highest ratings among
the closest users

20. Machine Learning: Similarity and metric learning
Hands on

21. Machine Learning: Similarity and metric learning
Is user based filtering good for
- scalability?
- sparse data?
- quickly changing data?

22. Machine Learning: Similarity and metric learning
Is user based filtering good for
- scalability?
- sparse data?
- quickly changing data?
No, it's better to use item
based filtering

23. Machine Learning: Similarity and metric learning
Euclidean distance for item based filtering:
nothing has changed!
- based on ratings got from
the users, we can measure
the distance among items
- we can recommend an
item to a user, getting the
items that are closer to
the highest rated by the
user

24. Machine Learning: Similarity and metric learning
Hands on

25. Machine Learning: Bayes' classifier
Bayes' theorem
P ( A∣B )=
P (B∣A)P (A )
P (B )
Example: given a company where 70% of developers use Java and 30%
use C++, and knowing that half of the Java developers always use
enhanced for loop, if you look at the snippet:
for (int j=0; j<100; j++) {
t = tests[j];
}
which is the probability that the developer who wrote it uses Java?

26. Machine Learning: Bayes' classifier
Bayes' theorem
P ( A∣B )=
P (B∣A)P (A )
P (B )
Example: given a company where 70% of developers use Java and 30%
use C++, and knowing that half of the Java developers always use
enhanced for loop, if you look at the snippet:
for (int j=0; j<100; j++) {
t = tests[j];
}
which is the probability that the developer who wrote it uses Java?
Hint:
A = developer uses Java
B = developer writes old for loops

27. Machine Learning: Bayes' classifier
Bayes' theorem
P ( A∣B )=
P (B∣A)P (A )
P (B )
Example: given a company where 70% of developers use Java and 30%
use C++, and knowing that half of the Java developers always use
enhanced for loop, if you look at the snippet:
for (int j=0; j<100; j++) {
t = tests[j];
}
which is the probability that the developer who wrote it uses Java?
Solution:
A = developer uses Java
B = developer writes old for loops
P(A) = prob. that a developer uses Java = 0.7
P(B) = prob. that any developer uses old for loop = 0.3 + 0.7*0.5 = 0.65
P(B|A) = prob. that a Java developer uses old for loop = 0.5
P (B∣A)P (A) 0.5⋅0.7
P (A∣B )=
=
=0.54
P (B )
0.65

28. Machine Learning: Bayes' classifier
Naive Bayes' classifier
- supervised learning
- trained on a set of known classes
- computes probabilities of elements to be in a class
- smoothing required
n
∏ P (c∣w i )
P c (w 1 , .... , w n )=
i =1
n
n
i =1
i =1
∏ P (c∣w i )+ ∏ (1−P (c∣w i ))

29. Machine Learning: Bayes' classifier
Naive Bayes' classifier
Example
- we want a classifier for Twitter messages
- define a set of classes: {art, tech, home, events,.. }
- trains the classifier with a set of alreay classified tweets
- when a new tweet arrives, the classifier will (hopefully)
tell us which class it belongs to

30. Machine Learning: Bayes' classifier
Hands on

31. Machine Learning: Bayes' classifier
Sentiment analysis
- define two classes: { +, - }
- define a set of words: { like, enjoy, hate, bore, fun, …}
- train a NBC with a set of known +/- comments
- let NBC classify any new comment to know if +/- performance is related to quality of training set

32. Machine Learning: Clustering
Clustering
- Unsupervised learning
- Different algorithms:
- Hierarchical clustering
- K-Means clustering
- ...
Common use cases:
- navigation habits
- online commerce
- social/political attitudes
- ...

33. Machine Learning: Clustering
K-Means clustering
K-Means aims at identifying
cluster centroids, such that an
item belonging to a cluster X,
is closer to the centroid of
cluster X than to the centroid
of any other cluster.

34. Machine Learning: Clustering
K-Means clustering
The algorithm requires a
number of clusters to start, in
this case 3. The centroids are
placed in the item space,
typically in random locations.

35. Machine Learning: Clustering
K-Means clustering
The algorithm will then assign
to each centroid all items that
are closer to it than to any
other centroid.

36. Machine Learning: Clustering
K-Means clustering
The centroids are then moved
to the center of mass of the
items in the clusters.

37. Machine Learning: Clustering
K-Means clustering
A new iteration occurs, taking
into account the new centroid
positions.

38. Machine Learning: Clustering
K-Means clustering
The centroids are again moved
to the center of mass of the
items in the clusters.

39. Machine Learning: Clustering
K-Means clustering
Another iteration occurs,
taking into account the new
centroid positions.

40. Machine Learning: Clustering
K-Means clustering
The centroids are again moved
to the center of mass of the
items in the clusters.

41. Machine Learning: Clustering
K-Means clustering
Another iteration occurs,
taking into account the new
centroid positions. Note that
this
time
the
cluster
membership did not change.
The cluster centers will not
move anymore.

42. Machine Learning: Clustering
K-Means clustering
The solution is found.

43. Machine Learning: Clustering
Hands on

44. Machine Learning: Neural networks
Neural networks
A logical calculus of the ideas immanent in nervous activity
by McCulloch and Pitts in 1943

45. Machine Learning: Neural networks
Neural networks
Feedforward Perceptron

46. Machine Learning: Neural networks
Neural networks
Logic operators with neural networks:
Threshold = 0
X0
-10
-10
-10
-10
X1
0
0
20
20
X2
0
20
0
20
Σ
-10
10
10
30
Result
0
1
1
1
OR operator

47. Machine Learning: Neural networks
Neural networks
Logic operators with neural networks:
Threshold = 0
X0
-30
-30
-30
-30
X1
0
0
20
20
X2
0
20
0
20
Σ
Result
which operator?

48. Machine Learning: Neural networks
Neural networks
Logic operators with neural networks:
Threshold = 0
X0
-30
-30
-30
-30
X1
0
0
20
20
X2
0
20
0
20
Σ
-30
-10
-10
10
Result
0
0
0
1
AND operator

49. Machine Learning: Neural networks
Hands on

50. Machine Learning: Neural networks
Neural networks
Backpropagation
Phase 1: Propagation
- Forward propagation of a training pattern's input
through the neural network in order to generate
the propagation's output activations
- Backward propagation of the propagation's output
activations through the neural network using the
training pattern target in order to generate the
deltas of all output and hidden neurons
Phase 2: Weight update
- Multiply its output delta and input activation to
get the gradient of the weight
- Bring the weight in the opposite direction of the
gradient by subtracting a ratio of it from the weight

51. Machine Learning: Neural networks
Neural networks
Multilayer perceptrons

52. Machine Learning: Neural networks
Hands on

53. Machine Learning: Genetic algorithms
Genetic algorithms
GA is a programming technique that mimics
biological evolution as a problem-solving strategy
Steps
- maps the variables of the problem into a sequence of
bits, a chromosome
Chromosome
- creates a random population of chromosomes
- let evolve the population using evolution laws:
- the higher the fitness, the higher the chance of breeding
- crossover of chromosomes
- mutation in chromosomes
- if otpimal solution is found or after n steps the
process is stopped

54. Machine Learning: Genetic algorithms
Genetic algorithms
Mutation
Crossover

55. Machine Learning: Genetic algorithms
Hands on

56. Machine Learning
Thanks!
The code is available on:
https://github.com/andreaiacono/MachineLearning