AI
This document consists of 19 printed pages.
Subject Code: 9618
Exam Date: 25/5/2024
Topics Included
CH18 - Artificial Intelligence (ai)
Instructions
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Student Details (must be filled)
Name
Jenish Jung Thapa
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Marking (for teachers only)
This worksheet includes 15 questions.
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The learning process that uses artificial neural networks that contains high
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number of hidden layers thats modelled according to the human brain. Deep
learning uses many layers to progressively extract higher level features from the
input. Its specialized form of machine learning.
It makes good use of unstructured data. Deep learning outperforms other methods if
data size is large.It enables machine to process data with a nonlinear approach. It is
effective at finding hidden patterns,patterns that human can't find out, too complex
patterns.It can provide more accurate outcome with more no of hidden layers.
Supervised learning allows data to be collected, or to produce output based
on previous experiences. It uses labelled input data.In supervised learning, known input and
associated outputs are given to train the machine. So, it is able to predict future outcomes based
on past data.
Unsupervised machine learning helps all kind of unknown patterns in
data to be found.It only requires input data to be given. It uses unlabelled input data. Its not trained
on right output.
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It enables deep learning to take place.When the problem you require to solve has higher complexity it requires
more layers to solve.To enable the neural network to learn and make decisions on its own.To improve the accuracy
of the result.
Artificial neural networks are intended to replicate the way human brain works. The values
are assigned for each connection between nodes. The data are input into input layer and are
passed into system. The data are analyzed at each subsequent layer where characteristics
are extracted and outputs are calculated. This process of learning and training is repeated
many times to achieve optimum output i.e. reinforcement learning takes place. Decisions can
be made without being specifically programmed. The deep learning network will have created
complex feature detectors. The output layer provide the results. Back propagation of errors
will be used to correct any errors that have been made.
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between nodes. AI can be defined as finding path in a graph. Graph can be analyzed using
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Artificial neural network can be represented using graph. The graph can provide the relationship
different algorithms as dijkstra or a* algorithm. Then back propagation and regression can be
used to predict the future outcomes by just looking at the input.
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The main purpose of Dijkstras and A* algorithm is to find the optimal,shortest and most
cost effective route between two nodes based on the distance,cost and time.
Deep learning,Supervised,unsupervised,reinforcement
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