AI - Robbi
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Transcript of AI - Robbi
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Using Data Mining
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Branch of Computer science which deals with intelligence
behavior, learning and adaptation in machines.
Artificial Intelligence
Man behind AI is John McCarthy
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Artificial intelligence (AI)
Computers with the ability to mimic or
duplicate the functions of the human brain
Artificial intelligence systems
The people, procedures, hardware, software,
data, and knowledge needed to developcomputer systems and machines that
demonstrate the characteristics of intelligence
Overview of AI
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Intelligent behavior Learn from experience
Apply knowledge acquired from experience
Handle complex situations
Solve problems when important information ismissing
Determine what is important
React quickly and correctly to a new situation
Understand visual images Process and manipulate symbols
Be creative and imaginative
Use heuristics
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Branches of AI
Perceptive system
A system that approximates the way a human sees,
hears, and feels objects
Vision system Capture, store, and manipulate visual images and
pictures
Robotics
Mechanical and computer devices that performtedious tasks with high precision
Expert system
Stores knowledge and makes inferences
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Learning system
Computer changes how it functions or reacts to
situations based on feedback
Natural language processing
Computers understand and react to statements and
commands made in a natural language, such as
English
Neural network
Computer system that can act like or simulate the
functioning of the human brain
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Artificialintelligence
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Most Famous application of
AI which is used in games,
puzzles and battles
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Use of Data Mining in Artificial Intelligence
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Process of semi-automatically analyzing large
databases to find patterns that are:
valid: hold on new data with some certainity
novel: non-obvious to the system
useful: should be possible to act on the item
understandable: humans should be able to
interpret the pattern Also known as Knowledge Discovery in Databases
(KDD)
Data Mining
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Credit ratings/targeted marketing: Given a database of 100,000 names, which persons are
the least likely to default on their credit cards?
Identify likely responders to sales promotions
Fraud detection
Which types of transactions are likely to be fraudulent,given the demographics and transactional history of aparticular customer?
Customer relationship management: Which of my customers are likely to be the most loyal, and
which are most likely to leave for a competitor? :
Need of Data Mining
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Basic Applications
Banking: loan/credit card approval
predict good customers based on old customers
Customer relationship management:
identify those who are likely to leave for a competitor.
Targeted marketing:
identify likely responders to promotions
Fraud detection: telecommunications, financial transactions from an online stream of event identify fraudulent events
Manufacturing and production:
automatically adjust knobs when process parameterchanges
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The US Government uses Data Mining to trackfraud
A Supermarket becomes an information broker
Basketball teams use it to track game strategy
Cross Selling
Target Marketing
Holding on to Good Customers Weeding out Bad Customers
AI along with DM
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Three fundamental AI techniques used in Data Mining
Knowledge Representation: Data Mining seeks to find out
interesting patterns from large volumes of data.
Knowledge Acquisition: The discovery process contains various
algorithms and methods to analyze data.
Knowledge Interference: The patterns discovered from data is to
be verified by various applications.
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Algorithm Mostly Used
Apriori Algorithm
Apriori is a classic algorithm for learning association rules.
Apriori is designed to operate on databases containing
Transactions.
Apriori algorithm helps to find associations in the form of
patterns which are very useful.
Apriori uses a "bottom up" approach, breadth-first searchand a tree structure to count candidate item sets efficiently.
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Applications of Apriori Algorithm
It helps in finding out associations and placement of different
products in a super market or a departmental store.
It helps in determine the customer preference in a video store.
It helps in detecting fraud over a series of transactions.
Its applications are numerous and the list goes on
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Certain methods Followed by AI in Data Mining
Clustering
Collaborative Filtering
Categorical Data Filtering
Association Filtering
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Thank You