This document provides an introduction to an online course on artificial intelligence (AI). It discusses that the course is offered through Udacity over 4 months and is instructed by Peter Norvig and Sebastian Thrun. The summary provides an overview of the key topics that will be covered in the course, including machine learning techniques like supervised and unsupervised learning, applications of AI such as computer vision and natural language processing, and how AI is used in areas like video games, music, and intelligent personal assistants.

1. Introduction To Artificial
Intelligence
BY:-DAKSH SEMWAL
CS-A 4 SEM. |ROLL NO-38(1011419)
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2. Website:-https://www.udacity.com
Instructors:-Peter Norvig,Sebastian Thrun
Duration:-4 Months
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3. Artificial Intelligence
• Artificial Intelligence (AI) is a field that has a long history but is still constantly and
actively growing and changing.
• In this course, you’ll learn the basics of modern AI as well as some of the
representative applications of AI.
• Along the way, we also hope to excite you about the numerous applications and
huge possibilities in the field of AI, which continues to expand human capability
beyond our imagination.
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4. Why this Course?
• Artificial Intelligence (AI) technology is increasingly prevalent in our everyday lives.
• It has uses in a variety of industries from gaming, journalism/media, to finance, as well as in the
state-of-the-art research fields from robotics, medical diagnosis, and quantum science.
• In this course we’ll learn the basics and applications of AI, including: machine learning,
probabilistic reasoning, robotics, computer vision, and natural language processing.
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5. Research
 Deduction, reasoning, problem solving
 Knowledge representation
 Default reasoning and the qualification problem
 The breadth of common-sense knowledge
 Planning
 Perception
 Motion and Manipulation
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6. Machine Learning
 Supervised Learning- The computer is presented with example inputs and their
desired outputs, given by a "teacher", and the goal is to learn a general rule
that maps inputs to outputs.
 Unsupervised Learning-No labels are given to the learning algorithm, leaving it on
its own to find structure in its input. Unsupervised learning can be a goal in itself
(discovering hidden patterns in data) or a means towards an end (feature
learning).
 Reinforcement Learning-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.
Another example is learning to play a game by playing against an opponent.
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7. Natural language processing
 Natural language processing gives machines the ability to
read and understand the languages that humans speak.
 A sufficiently powerful natural language processing system
would enable natural language user interfaces and the
acquisition of knowledge directly from human-written
sources, such as newswire texts.
 Some straightforward applications of natural language
processing include information retrieval (or text
mining), question answering and machine translation.
 A common method of processing and extracting meaning
from natural language is through semantic indexing.
Increases in processing speeds and the drop in the cost of
data storage makes indexing large volumes of abstractions of
the user's input much more efficient.
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A parse tree represents
the syntactic structure of a
sentence according to
some formal grammar.

8. A* Search
 In computer science, A* (pronounced as "A star" ( listen)) is a computer algorithm
that is widely used in pathfinding and graph traversal, the process of plotting an
efficiently traversable path between multiple points, called nodes.
 Noted for its performance and accuracy, it enjoys widespread use. However, in
practical travel-routing systems,
 It is generally outperformed by algorithms which can pre-process the graph to
attain better performance, although other work has found A* to be superior to
other approaches.
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9. Bayesian Network
 A Bayesian network is a probabilistic graphical model (a type of statistical model) that represents a
set of random variables and their conditional dependencies via a directed acyclic graph (DAG).
 For example, a Bayesian network could represent the probabilistic relationships between diseases
and symptoms. Given symptoms, the network can be used to compute the probabilities of the
presence of various diseases.
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10. Application Of A.I
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11.  A.I In Video Games
 In video games, artificial intelligence is used to generate intelligent behaviours primarily in non-
player characters (NPCs), often simulating human-like intelligence.
 The techniques used typically draw upon existing methods from the field of artificial intelligence
(AI).
 However, the term game AI is often used to refer to a broad set of algorithms that also include
techniques from control theory, robotics, computer graphics and computer science in general.
 Example., Far-Cry 2 and Halo
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12.  Music And Artificial Intelligence
 Research in artificial intelligence (AI) is known to have impacted medical diagnosis, stock trading,
robot control, and several other fields. Perhaps less popular is the contribution of AI in the field of
music.
 Nevertheless, artificial intelligence and music (AIM) has, for a long time, been a common subject in
several conferences and workshops, including the International Computer Music Conference, the
Computing Society Conference and the International Joint Conference on Artificial Intelligence.
 In fact, the first International Computer Music Conference was the ICMC 1974, Michigan State
University, East Lansing, USA Current research includes the application of AI in music composition,
performance, theory and digital sound processing.
 Example., Omax and Melomics
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13.  Intelligent Personal Assistant
 An intelligent personal assistant (or simply IPA) is a software agent that can perform tasks or services
for an individual.
 These tasks or services are based on user input, location awareness, and the ability to access
information from a variety of online sources (such as weather or traffic conditions, news, stock prices,
user schedules, retail prices, etc.).
 Examples of such an agent are Apple's Siri, Google's Google Now, Amazon Echo, Microsoft's Cortana,
Facebook's M (app) and Google Allo(App)
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15. THANK YOU
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