Page 258 - AI Ver 1.0 Class 9
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2. What is clustering? Give an example.
Ans. This is a machine learning approach where the machine generates its own rules or algorithms to differentiate amongst
the given dataset to achieve the pre-decided goal. The data fed to such a model is usually unlabelled or random and
thus, the developer feeds in the data directly into the machine and instructs it to build its own algorithm. The machine
then finds patterns or trends out of the training dataset and clusters the ones which follow the same pattern. The
output rules might be very different to what was expected as the machine has its own way of recognising patterns. For
example, if you have a random data of stray dogs which live in your locality, since you are unable to find any meaningful
pattern amongst them, you would feed their data into the clustering algorithm. The algorithm would then analyse the
data and divide them into clusters according to their similarities based on the trends noticed. The clusters are then
given as the output. Clustering works on discrete dataset.
3. Mention three features of neural networks.
Ans. Neural network systems are modelled on the human brain and nervous system. Following are three features of neural
network are:
• They are able to automatically extract features without input from the programmer.
• Every neural network is essentially a machine learning algorithm.
• It is useful when solving problems for which the dataset is very large.
4. Give the differences between rule-based approach and machine learning approach.
Ans. Under the rule-based approach, the developer feeds in data along with some ground rules to the model. The model
gets trained with these inputs and gives out answers in the form of predictions. This approach is commonly used when
we have a known dataset or labelled dataset.
Whereas under the machine learning approach the developer feeds in data along with the answers. The machine then
designs its own algorithms and methodologies to match the data with answers and give out the rules. This approach is
commonly used when the data is unknown/random or unlabelled.
5. Give three applications of neural networks.
Ans. Applications of neural networks are:
• Facial recognition: Cameras on smartphones these days can estimate the age of the person based on their facial
features. This is neural networks at play. First differentiating the face from the background and then correlating the
lines and spots on your face to a possible age. For example, Facebook uses facial recognition powered by artificial
neural networks to suggest to you whom you should tag in the post.
• Forecasting: Neural networks are trained to understand the patterns and detect the possibility of rainfall or rise in
stock prices with high accuracy.
• Music Composition: Neural networks can even learn patterns in music and train themselves enough to compose
fresh tunes.
C. Competency-based/Application-based questions:
1. Assertion: Machine Learning enables machines to improve at tasks with experience.
Reason: If a Machine cannot improvise it is at par with Human Intelligence.
(i) Both A and R are correct and R is the correct explanation of A.
(ii) Both A and R are correct but R is NOT the correct explanation of A.
(iii) A is correct but R is incorrect.
(iv) A is incorrect but R is correct.
Ans. (iii) A is correct but R is incorrect.
256 Touchpad Artificial Intelligence-IX

