This document provides summaries of topics related to artificial intelligence including machine learning, knowledge representation, robotics, computer vision, knowledge reasoning, natural language processing, automation, and neural networks. It describes machine learning as teaching computers by giving them access to data to set their own behavior. Knowledge representation is defined as representing knowledge through facts and representations of facts. Robotics combines advances in engineering, materials science, manufacturing and algorithms. Computer vision involves understanding digital images and video. Knowledge reasoning refers to acquiring knowledge through experience or education. Natural language processing involves interactions between computers and human languages. Automation uses control systems to operate equipment with minimal human intervention. Neural networks are computational models inspired by biological neural networks in the nervous system.

1. Submitted to:-
Prof. Vandana Gautam
Submitted By:-
Iqbal Khan

2. Contents:-
 Machine Learning
 Knowledge Representation
 Robotics
 Computer Vision
 Knowledge Reasoning
 Natural Language Processing
 Automation
 Neural Network

3. Machine Learning:
Machine learning is a basic sub-part of
Artificial intelligence. If we are teaching
to computer , it is artificial intelligence
and machine learning is if we are giving
access of data to AI system. This system
set their behavior accordingly then it is
called Machine learning.

4. Knowledge Representation:
It is a study of ways of how knowledge
is actually represented or picturized.
For representation of knowledge two
entities are important:-
 1. Fact
 2. Representation of facts

5. Approaches of knowledge Representation:
1. Simple Relational Knowledge
2. Inheritable Knowledge
3. Infereutial Knowledge
4. Procedural Knowledge

6. Robotics:
 Robotics is a confluence science using the
continuing advancements of mechanical
engineering, material science, sensor
fabrication, manufacturing techniques, and
advanced algorithms. The study and
practice of robotics will expose a dabbler or
professional to hundreds of different
avenues of study .

7. Computer Vision:
 Computer vision is an interdisciplinary field that deals
with how computers can be made to gain high-level
understanding from digital images or videos. From the
perspective of engineering, it seeks to automate tasks that
the human visual system can do.
 Computer vision tasks include methods
for acquiring, processing, analyzing and understanding
digital images, and extraction of high-dimensional data
from the real world in order to produce numerical or
symbolic information, e.g., in the forms of
decisions.Understanding in this context means the
transformation of visual images.

8. Knowledge Reasoning:
 Knowledge is a familiarity, awareness, or understanding of
someone or something, such as facts, information, descriptions,
or skills, which is acquired
through experience or education by perceiving, discovering,
or learning.
 Knowledge can refer to a theoretical or practical understanding
of a subject. It can be implicit (as with practical skill or expertise)
or explicit (as with the theoretical understanding of a subject); it
can be more or less formal or systematic.In philosophy, the study
of knowledge is called epistemology; the
philosopher Plato famously defined knowledge as "justified true
belief", though this definition is now thought by some analytic
philosophers to be problematic because of the Gettier
problems while others defend the platonic definition.However,
several definitions of knowledge and theories to explain it exist.

9. Natural Language Processing:
 Natural language processing (NLP) is a subfield
of computer science, information engineering,
and artificial intelligence concerned with the
interactions between computers and human (natural)
languages, in particular how to program computers to
process and analyze large amounts of natural
language data.
 Challenges in natural language processing frequently
involve speech recognition, natural language
understanding, and natural language generation.

10. Automation:
 Automation is the technology by which a process or procedure
is performed without human assistance. Automation [ or
automatic control is the use of various control systems for
operating equipment such as machinery, processes in factories,
boilers and heat treating ovens, switching on telephone
networks, steering and stabilization of ships, aircraft and other
applications and vehicles with minimal or reduced human
intervention. Some processes have been completely automated.
 Automation covers applications ranging from a
household thermostat controlling a boiler, to a large industrial
control system with tens of thousands of input measurements
and output control signals. In control complexity it can range
from simple on-off control to multi-variable high level
algorithms.

11. Neural Network:-
An Artificial Neuron Network (ANN),
popularly known as Neural Network is a
computational model based on the
structure and functions of
biological neural networks. It is like an
artificial human nervous system for
receiving, processing, and transmitting
information in terms of Computer Science.

13. Biological Neural Network:-
 Artificial NN draw much of their inspiration from the
biological nervous system. It is therefore very useful to
have some knowledge of the way this system is
organized.
 The control unit - or brain - can be divided in different
anatomic and functional sub-units, each having certain
tasks like vision, hearing, motor and sensor control.
The brain is connected by nerves to the sensors and
actors in the rest of the body.

15. The brain consists of a very large number of
neurons, about 1011 in average. These can be
seen as the basic building bricks for the central
nervous system (CNS). The neurons are
interconnected at points called synapses. The
complexity of the brain is due to the massive
number of highly interconnected simple units
working in parallel, with an individual neuron
receiving input from up to 10000 others.

16. Artificial Neural Network:-

17. Thank You