ARTIFICIAL
INTELLIGENCE
PRESENTATION
Learning, Genetics, and Neural
Networks
Team Members
Kanishk Bhadauria
01214802719
Muskan Garg
02614802719
Nikhil Jha
03414802719
Aparna Jha
03914802719
Learning
Leraning is done by viewing listening, interactions, studying and by
experience. Leeearing providesus the power to reason, ability to handle
new situations and enables us to act in an intelligent way. Human
beings are intelligent as they possess knowledge of world. Similarly,
making a machine intelligent means it should have the power of
learning.
Leraning is essentialfor unknown environments
Learning is uselful as a system construction method.. Learning
modifies the agent's decision mechanism to improve
performance
There are three general categories of learning
that artificial intelligence & machine learning
utilizes to actually learn
Supervised Learning: The machine has a “teacher” who guides it by
providing sample inputs along with the desired output. The machine then
maps the inputs and the outputs. This is similar to how we teach very young
children with picture books. Almost all of the AI machines we have today
have used this form of learning (from speech recognition to self-driving
cars).
Unsupervised Learning: This is the most important and most difficult
type of learning and would be better titled Predictive Learning. In this
case the machine is not given any labels for its inputs and needs to
“figure out” the structure on its own. This is similar to how babies
learn early in life. For example they learn that if an object in space is
not supported it will fall
Reinforcement Learning: It is defined as “a computer program interacts with
a dynamic environment in which it must perform a certain goal (such as
driving a vehicle), without a teacher explicitly telling it whether it has come
close to its goal. “. The agent is awarded for good responses and punished
for bad ones.
Methods of Learning in context of machines
are as follow
Rote Learning: Rote learning is the process of memorizing specific new items as they are
encountered. The basic idea is simple and easy to realize within a computer program.
Each time a new and useful piece of information is encountered, it is stored away for
future use.
Learning by taking advice: Both humans as well as machines can learn through advice.
This type is the easiest and simple way of learning. In this type of learning, a programmer
writes a program to give some instructions to perform a task to the computer. Once it is
learned (i.e. programmed), the system will be able to do new things.
Learning by induction:
Inductive learning involves using evidence to determine the
outcome. Inductive reasoning refers to using specific cases to determine general
outcomes, e.g. specific to general.
Learning by Deduction (or Relevance-based learning): Unlike inductive learning,
which is based on the generalization of specific facts, deductive learning uses the
already available facts and information in order to give a valid conclusion. It uses a
top-down approach. The one major thing to note is that in deductive learning, the
results are certain i.e, it is either yes or no. Whereas it’s probability-based on inductive
learning i.e, it can range from strong to weak. Since, deductive reasoning works on preavailable logical facts, let’s have a look.
a. All carnivores eat meat.
b. Lion is a carnivore.
Conclusion: – Lion eats meat.
Learning by Analogy: The analogy method in AI is teaching a machine a new topic by
connecting it with familiar information. For example, we might rely on portraying the
white blood cells of our body as soldiers in our defense system.
Explanation Based Learning: Explanation based learning has ability to learn from a
single training instance. Instead of taking more examples the explanation based
learning is emphasized to learn a single, specific example.
Genetic Algorithm
Genetic Algorithm is one of the heuristic algorithms.
They are used to solve optimization problems.
They are inspired by Darwin’s Theory of Evolution.
They are an intelligent exploitation of a random search.
Although randomized, Genetic Algorithms are by no means random.
Genetic Algorithm works in the
following steps
Step 1
Randomly
Step 2
generate
a
set
of
Using a fitness function, test
possible solutions to a problem.
each possible solution
Represent each solution as a fixed
against
length character string.
evaluate them.
Step 3
Keep the best solutions.
Use best solutions to generate
new possible solutions.
the
problem
to
Step 4
Repeat the previous two steps
until
either
an
acceptable
solution is found or until the
algorithm
has
completed
iterations
through
a
its
given
number of cycles / generations
Basic Operators
The basic operators of
Genetic Algorithm are
1. Selection (Reproduction)
It is the first operator applied on the population.
It selects the chromosomes from the population of parents
to cross over and produce offspring. It is based on evolution theory of
“Survival of the fittest” given by Darwin.
There are many techniques for reproduction or selection operator such as:
1. Tournament selection
2. Ranked position selection
3. Steady state selection etc.
2. Cross Cover
Population gets enriched with better individuals after reproduction phase.
Then crossover operator is applied to the mating pool to create better strings.
Crossover operator makes clones of good strings but does not create new ones.
By recombining good individuals, the process is likely to create even better individuals
3. Mutation
Mutation is a background operator.
Mutation of a bit includes flipping it by changing 0 to 1 and vice-versa.
After crossover, the mutation operator subjects the strings to mutation.
It facilitates a sudden change in a gene within a chromosome.
Thus, it allows the algorithm to see for the solution far away from the current ones.
It guarantees that the search algorithm is not trapped on a local optimum.
Its purpose is to prevent premature convergence and maintain diversity within the
population.
Advantages
Genetic Algorithms offer the following advantages
Point 1
Point 2
Genetic Algorithms are better than
conventional AI.
This is because they are more robust.
They do not break easily unlike
older AI systems.
They do not break easily even
in the presence of reasonable
Point 3
While performing search in multi modal
state-space or large state-space,
Genetic algorithms has significant
benefits over other typical search
optimization techniques.
noise or if the inputs get
change slightly.
Flow Chart
The following
flowchart represents
how a genetic
algorithm works
Neural Networks
Neural networks are a series of
the
They are used in a variety of
operations of an animal brain to
applications in financial services,
recognize relationships between
from forecasting and marketing
vast amounts of data.
research to fraud detection and risk
algorithms
that
mimic
assessment.
Neural
process
networks
layers
with
are
several
known
as
As such, they tend to resemble the
"deep" networks and are used for
connections
of
neurons
deep learning algorithms
synapses found in the brain.
and
A simple Neural Network
Input layer
Hidden layer
Output layer
Convolutional Neural Network:
A convolutional neural network is one adapted for
analyzing and identifying visual data such as digital
images or photographs.
Recurrent Neural Network:
A recurrent neural network is one adapted for analyzing
time series data, event history, or temporal ordering.
Deep Neural Network:
Also known as a deep learning network, a deep neural
network, at its most basic, is one that involves two or more
processing layers.
Applications of Neural Networks
Neural Networks are regulating some key sectors including finance, healthcare,
and automotive. As these artificial neurons function in a way similar to the
human brain. Here are some of the key applications of Neural Networks Facial Recognition.
Stock Market Prediction.
Social Media.
Aerospace.
Defence.
Healthcare.
Signature Verification and Handwriting Analysis.