4. Introduction to Supervised, Unsupervised and Reinforcement
Learning
ü The amount
of data generated in the world today is very huge. This data is generated not
only by humans but also by smartphones, computers and other devices. Based on
the kind of data available and a motive present, certainly, a programmer will
choose how to train an algorithm using a specific learning model.
· Machine Learning is a
part of Computer Science where the efficiency of a system improves itself by
repeatedly performing the tasks by using data instead of explicitly programmed
by programmers. Further let us understand the difference between three
techniques of Machine Learning- Supervised, Unsupervised and Reinforcement
Learning.
How Machine Learning Works?
Types of Learning Algorithms
i.
Supervised
learning
ii.
Unsupervised
learning
iii.
Reinforcement
learning
Supervised Learning
·
In
Supervised learning, an AI system is presented with data which is labeled,
which means that each data tagged with the correct label.
·
The goal
is to approximate the mapping function so well that when you have new input
data (x) that you can predict the output variables (Y) for that data.
·
As shown
in the example, we have initially taken some data and marked them as ‘Spam’ or
‘Not Spam’. This labeled data is used by the training supervised model, this
data is used to train the model.
·
Once it
is trained we can test our model by testing it with some test new mails and
checking of the model is able to predict the right output.
Types of Supervised learning
i.
Classification: A classification problem is when the output
variable is a category, such as “red” or “blue” or “disease” and “no disease”.
ii.
Regression: A regression problem is when the output
variable is a real value, such as “dollars” or “weight”.
Unsupervised Learning Algorithm
·
In
unsupervised learning, an AI system is presented with unlabeled, uncategorized
data and the system’s algorithms act on the data without prior training.
·
In the
example, we have given some characters to our model which are ‘Ducks’ and ‘Not
Ducks’.
·
In our
training data, we don’t provide any label to the corresponding data.
·
The
unsupervised model is able to separate both the characters by looking at the
type of data and models the underlying structure or distribution in the data to learn more about it.
can
Types of Unsupervised Learning
i.
Clustering: A clustering problem is where you want to
discover the inherent groupings in the data, such as grouping customers by
purchasing behavior.
ii.
Association: An association rule learning problem is
where you want to discover rules that describe large portions of your data,
such as people who buy X also tend to buy Y.
Reinforcement Learning
·
A
reinforcement learning algorithm, or agent, learns by interacting with its
environment.
·
The agent
receives rewards by performing correctly and penalties for performing
incorrectly.
·
The agent
learns without intervention from a human by maximizing its reward and
minimizing its penalty.
·
It is a
type of dynamic programming that trains algorithms using a system of reward and
punishment.
In the example, we can see that the agent is
given 2 options i.e. a path with water or a path with fire.
·
A
reinforcement algorithm works on reward a system i.e. if the agent uses the
fire path then the rewards are subtracted and agent tries to learn that it
should avoid the fire path.
·
If it had
chosen the water path or the safe path then some points would have been added
to the reward points, the agent then would try to learn what path is safe and
what path isn’t.
·
It is
basically leveraging the rewards obtained, the agent improves its environment
knowledge to select the next action.
Applications
of Machine Learning
•Recognizing
patterns:
ü Facial
identities or facial expressions
ü Handwritten
or spoken words
ü Medical
images
•Generating
patterns:
ü Generating
images or motion sequences
•Recognizing
anomalies:
ü Unusual
sequences of credit card transactions
ü Unusual
patterns of sensor readings in a nuclear power plant or unusual sound in your
car engine.
•Prediction:
ü Future
stock prices or currency exchange rates
Influence
of Disciplines on Machine Learning
i.
Artificial intelligence
Learning
symbolic representations of concepts. Machine learning as a search problem.
Learning as an approach to improving problem solving. Using prior knowledge
together with training data to guide learning.
ii.
Bayesian methods
Bayes'
theorem as the basis for calculating probabilities of hypotheses. The naive
Bayes classifier. Algorithms for estimating values of unobserved variables.
iii.
Computational complexity theory
Theoretical
bounds on the inherent complexity of different learning tasks, measured in
terms of the computational effort, number of training examples, number of
mistakes, etc. required in order to learn.
iv.
Control theory
Procedures
that learn to control processes in order to optimize predefined objectives and
that learn to predict the next state of the process they are controlling.
v.
Information theory
Measures
of entropy and information content. Minimum description length approaches to
learning. Optimal codes and their relationship to optimal training sequences
for encoding a hypothesis.
vi.
Philosophy
Occam's
razor, suggesting that the simplest hypothesis is the best. Analysis of the
justification for generalizing beyond observed data.
vii.
Psychology and neurobiology
The
power law of practice, which states that over a very broad range of learning
problems, people's response time improves with practice according to a power
law. Neurobiological studies motivating artificial neural network models of
learning.
viii.
Statistics
Characterization
of errors (e.g., bias and variance) that occur when estimating the accuracy of
a hypothesis based on a limited sample of data. Confidence intervals,
statistical tests.
|
|
Supervised
learning
|
Unsupervised
learning
|
Reinforcement
learning
|
|
Definition
|
Makes
predictions from data
|
Segments
and groups data
|
Reward-punishment
system and interactive environment
|
|
Types of
data
|
Labelled data
|
Unlabeled data
|
Acts according to a policy
with a final goal to reach (No or predefined data)
|
|
Commercial
value
|
High
commercial and business value
|
Medium
commercial and business value
|
Little
commercial use yet
|
|
Types of
problems
|
Regression and
classification
|
Association and
Clustering
|
Exploitation or Exploration
|
|
Supervision
|
Extra
supervision
|
No
|
No
supervision
|
|
Algorithms
|
Linear Regression, Logistic
Regression, SVM, KNN and so forth
|
K – Means
clustering,
C – Means, Apriori
|
Q –
Learning,
SARSA
|
|
Aim
|
Calculate
outcomes
|
Discover underlying
patterns
|
Learn a
series of action
|
|
Application
|
Risk Evaluation, Forecast
Sales
|
Recommendation System, Anomaly
Detection
|
Self-Driving Cars, Gaming,
Healthcare
|
Which is the better Machine Learning
technique?
ü
We
learned about the three main members of the machine learning family essential
for deep learning. Other kinds of learning are also available such as
semi-supervised learning, or self-supervised learning.
ü
Supervised,
unsupervised, and reinforcement learning, are all used for different to
complete diverse kinds of tasks. No single algorithm exists that can solve
every problem, as problems of different natures require different approaches to
resolve them.
ü
Despite
the many differences between the three types of learning, all of these can be
used to build efficient and high-value machine learning and Artificial
Intelligence applications. All techniques are used in different areas of
research and development to help solve complex tasks and resolve challenges.
