Lecture 8 learning types on machine learning

1. Computers Eng. & systems students
By
Prof. Amira Yassien

2. Learning Paradigms

3. 1. Supervised learning
a. Regression
Example:
 Learning to predict houses’ prices
 Suppose you collect a dataset of houses’ prices in a certain
geographical area.
 Suppose you collect statistics about how much houses cost
according to the square footage (feet2) of the house.
x
x
$
x x
x
x
feet2
Cost will be
If my house is
here
x
x
x
x

4. x
x
$
x x
x
x
feet2
Cost will be
If my house is
here
x
x
x
x

5.  The reason for calling this a supervised problem is that
we provide the algorithm a dataset of a punch of houses’
sizes and actual prizes.
 We simply supervise the algorithm, we give the algorithm
the quit right answer for the prices and we want the
algorithm to learn the association between the i/ps and
o/ps so it gives us more about the right answers.
 This was an example of what is called a REGRESSION
problem.
 The term regression reverse the fact that the o/p you are
trying to predict is a continuous value of the price.

6. 1. Supervised learning
b.classification
 Another case of supervised learning problem is the
classification in which the variable you are trying to
predict is discrete rather than continuous.
Example:
 Let's say you collect a dataset on a cancer tumor and
you want the program o/p to predict whether the certain
tumor is malignant (harmful bad tumor) or benign.
 So we collect a number of features of that tumor for the
sack of having a simple explanation.

7. +1 _
Malignant
x x x x x
x x x x
Tumor size
Variables here take values either 0 or 1.
 The features we collect here is the size of the tumor, depending on the
size of the tumor we are trying to decide whether the tumor malignant
or not.
 The Y axis is either 0 or 1 so the dataset may look like this:

8. Example:
 More generally there's other examples that use more than 1 i/p
variable (multi-i/p) to try to predict whether the tumor is malignant or
not.
 So patients of different ages having tumors of different sizes will
represents the new dataset.
age
x x
x x o
x x x
x x
o o
o o o o
o o oo o
Tumor size
o malignant
x benign

9.  You want a program to help you predict whether the new patient of
specific age and having certain tumor size whether he has malignant
or benign tumor.
 One program may suggest to separate the 2 classes of the tumors
using a straight line(figure)
 So if we have a patient that lie as figure (green point) so u may
classify the patient tumor as benign rather than malignant.
 So we could predict the tumor type based on 2 i/ps.
 There are other features (other medical properties ) that the program
can use regarding the properties of the tumor like thickness,
uniformity of cell size, and uniformity of cell shapes, adhesion and
so on.

10.  One of the most interesting things that M.L. is concerned with is what if
your data doesn't lie in a two-dimensional or three-dimensional or sort
of even a finite dimensional space.
 What if your data actually lies in an infinite dimensional space? Our
plots here are two-dimensional space.

11. Support Vector Machines
SVM
 SVM actually takes data and maps data to an infinite dimensional
space and then does classification using not only two features like
we've done, but an infinite number of features.
 And so it turns out that one of the most successful classes of
machine learning algorithms: Support Vector Machines

12. Learning theory
we want to explain how learning programs work so we can apply these
learning programs as effective as possible.
We need to know
 How much training data we need in order for your learning program to
recognize the patterns between the square foot inch of the house (size of
the house) and its price.
 If you are trying to design a learning program you should spend more time
collecting the data.
 In Machine Learning we need to know how to take the tool of machine
learning and really apply them and learn how to use them well.

13. Supervised learning
 An example of a vehicle known as ALVINN.
 It was done using algorithm called Neural Network.
 The essential learning algorithm for this is called
gradient descent,
 This is supervised learning because it's learning from
a human driver, in which a human driver shows that
we're on this segment of the road, I will steer at this
angle. This segment of the road, I'll steer at this angle.
And so the human provides the number of "correct"
steering directions to the car, and then it's the job of the
car to try to learn to produce more of these "correct"
steering directions that keeps the car on the road.

14.  And as the algorithm collects more examples and learns of
a time, it starts to more confidently choose a steering
direction.
 ALVINN was an example on supervised learning, and in
particular it was a regression problem, because the vehicle
is trying to predict a continuous value variables of a
continuous value steering directions.

15. Practical Application
 The ALVINN ( Autonomous Land Vehile In a Neural
Network) project.
 The ALVINN takes road images from a camera and a laser
range finder as an input (video and range information).
 It produces as o/p the direction the vehicle should travel in
order to follow the road.
 The NN performed much better after it had been trained to
recover after making mistakes during driving.

17. Unsupervised learning
 In supervised learning the data you have gives you the right
answer to your patient and you want the program just to produce
more the same.
 In contrast, in unsupervised learning problem, you are given a
dataset and I’m not going to tell you what the right answer is,
just want you to find some interesting structure in this data.

18.  Clustering would be one example of unsupervised learning, as the
program finds structure in the data in the form of data are being
partitioned into e.g. two clusters.
 Unsupervised learning is also used in many problems,
 For example applying an unsupervised learning algorithm for
trying to understand genes data, trying to look in genes in
individuals and group them into clusters based on properties of
how do genes respond to different experiments.
t x x
x x x
x x x
x x
xx

19.  Another Application of unsupervised learning is Image processing.
 If you give this sort of data, say an image, to a certain unsupervised
learning algorithm they will then learn to group pixels together and
say those sort of pixel seem to belong together and the images you see
in the bottom are grouping to what the program has done to group
different pixels together,
 This sort of clustering algorithm (unsupervised learning algorithm)
which learns to group pixels together tends out to be useful for many
application in computer vision image processing.

20. An example of unsupervised learning
 Imagine a party and lots of people standing all over ,
everyone is talking,
 You find it so difficult hearing a person talking in front
of you. the problem here is that with everybody talking
can you separate voices of persons you are talking with?
 Let that the party is attended by two people and we put 2
microphones in a room, microphones are at a slightly
different distances and 2 people speak in a slightly
different volumes,

21.  each microphone will pick an overlapping combination of these 2
people's voices so slightly different overlapping combination of
voices so speaker 1 maybe over loud on micro 1 and speaker 2
maybe over loud on micro 2,
 The question is: given these microphone recording can we separate
the 2 voices ?
MIC 1
MIC 2
Speaker 1
Speaker 2

22. Conclusions
 Unsupervised learning doesn't give the right answer
however we ask the program what structure is there in
these data.
 You will discover that this data can actually be
explained by 2 independent speakers speaking at the
same time and can further separate those 2 voices.
 That learning algorithm sounds to be so complicated, to
take these overlapping audio stream and separate them
out? But how complicated is it????
 To implement this ( one line of code in MATLAB)
several of lines and pages by C or JAVA

23.  This specific unsupervised learning program are also applied to
a punch of other text processing or understanding functional
grading and machine data problems such as, the magneto-
encephalogram would be an EEG data.

24. Reinforcement learning
 This refers to problems where you don't do one shot
decision-making.
 So, for example, in the supervised learning cancer
prediction problem, you have a patient come in, you
predict that the cancer is malignant or benign. Then based
on your prediction, maybe the patient lives or dies
 So you make a decision and then there's a consequence.
You either got it right or wrong.
 In reinforcement learning problems, you are usually asked
to make a sequence of decisions over time.

25.  So reinforcement learning is — I think of it as a
way for you to specify what you want to be done,
so you have to specify when to “reward" and when
to “punish".
 And then it's up to the learning algorithm to figure
out how to maximize the reward signals and
minimize the punishments.
 Reinforcement learning is applied to other
problems in robotics.

26.  It's applied in web crawling.
 A punch of other problems in Robotics using reinforcement
learning :
1. A four legged robotic dog
2. A robotic snakes: using learning algorithm to teach the
snake to climb obstacle
3. Use learning algorithm to teach a car how to drive in high
speeds off road avoiding obstacles
4. Teach a robot how to get over an obstacle
 All of these robots are difficult to hand code controller for
but
 Using these source of learning algorithm in its short order
you can get a robot to do amazing things

27. Web crawling and reinforcement learning
 Amount of accessible information on World Wide Web is
increasing rapidly, so that a general-purpose search engine
cannot index everything on the Web.
 Focused crawlers have been proposed as a potential
approach to overcome the coverage problem of search
engines by limiting the domain of concentration of them.
 Focused crawling is a technique which is able to crawl
particular topical portions of the WWW quickly and
efficiently by following only the most interesting links and
not having to explore all Web pages. e.g., "crawl pages
about baseball", or "crawl pages with large PageRank"

29.  A Web crawler, sometimes called a spider, is an Internet
bot that systematically browses the www, typically for the
purpose of Web indexing (web spidering).
 Web search engines and some other sites use Web crawling or
spidering software to update their web content or indices of
others sites' web content.
 Web crawlers can copy all the pages they visit for later
processing by a search engine which indexes the downloaded
pages so the users can search much more efficiently.

30. Learning paradigm
supervised:
1- Learning with a Teacher.
2- fast but unnatural
regression classification
unsupervised
1-no external teacher or
critic to oversee the
learning process.
2- slow, natural
clustering
Reinforcement
continued interaction with
the environment in order to
minimize a scalar index of
performance
1- Credit assignment
problem
2-Delay response.