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4.  Training a neural network often requires:                                         [CBSE Handbook]
                       a.  A small set of labelled data samples
                       b.  A significant amount of data and computational resources
                       c.  A specific set of programming instructions
                       d.  A human expert to guide the learning process

                    5.  What does unsupervised learning primarily aim to achieve?
                       a.  Predict specific labels for new data          b.  Identify hidden patterns or clusters in the data

                       c.  Replace rule-based systems entirely           d.  Solve predefined problems using training data
                    6.  Which of the following is NOT a sub-category of supervised learning?       [CBSE Sample Paper, 2021]

                       a.  Classification                                b.  Regression
                       c.  Clustering                                    d.  Predictive Modelling

                    7.  Which of the following is an example of reinforcement learning?
                       a.  Identifying spam emails
                       b.  Predicting house prices
                       c.  A robot learning to pick objects through trial and error

                       d.  Grouping customers based on purchase history

                    8.  Which of these is an example of a classification problem?
                       a.  Predicting house prices                       b.  Grouping customers based on spending
                       c.  Determining if an email is spam               d.  Predicting the temperature

                 B.  Fill in the blanks.

                    1.  The learning-based approach is typically used when the dataset is ……….……................ and too random.
                    2.  The goal of ……….……................ learning is to find hidden patterns or clusters in the data.
                    3.  Supervised learning models use a ……….……................ dataset to train the machine.

                    4.  Classification models work with ……….……................ datasets, whereas regression models predict ……….……................ values.
                    5.  Neural Networks are able to extract data ……….……................ automatically without needing the input of the programmer.
                    6.  ……….……................ are made up of layers of neurons, just like the human brain that consists of millions of neurons.

                    7.  An ……….……................ in its training phase is capable of learning by recognising patterns in data which is later used to
                       generate the desired output.
                    8.  The  layer  present  in-between  input  and  output  layers  is  called  the  ……….……................  which  perform  most of  the
                       computations required by our network.

                 C.  State whether these statements are true or false.

                    1.  A clustering algorithm is applied to group customers based on their purchase behaviour.    ……….……
                    2.  With AI, it’s not possible for machines to learn from the experience.                     ……….……
                    3.  ANN is made up of two basic layers – Input and Output.                                    ……….……
                    4.  A labelled dataset is the information which is tagged with identifiers of data.           ……….……
                    5.  The data that is used to predict the model is called testing data.                        ……….……

                    6.  Association rule is used in supervised learning to label datasets.                        ……….……


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