Page 129 - Artificial Intellegence_v2.0_Class_11
P. 129

2.   Differentiate between supervised and unsupervised learning. Also, name one algorithm and one application of
                          each learning.
                     Ans:
                                           Supervised Learning                          Unsupervised Learning
                           Supervised learning algorithms are trained using labelled   Unsupervised learning algorithms are
                           data.                                               trained using unlabelled data.
                           Input data is provided to the model along with the output.  Only input data is provided to the model.

                           Algorithm – Regression                              Algorithm – Clustering
                           Application: Fraud Detection                        Application: Customer segmentation


                       3.   Every time you buy a product on Amazon, it gives ‘suggestions’ for related products that you could buy by saying
                          “People who bought _______ (product) also bought __________ (list of suggestions)”. What is this system called? What
                          kind of machine learning is being used here?

                     Ans:  The system being used is the recommender system. Recommender systems are designed to recommend things
                          to the user based on their likes, trends, etc. These systems predict the product(s) that the users are most likely to
                          purchase and are of interest to. Companies like Netflix, Amazon, etc. use recommender systems to help their users
                          to identify the correct product or movies for them. Recommender systems use unsupervised machine learning. This
                          helps websites improve user engagement.
                       4.  Briefly explain the three layers of the Neural Network with the help of a diagram.

                     Ans:  A Neural Network is divided into multiple layers and each layer        Hidden
                          is further divided into several blocks called nodes. Each node has
                          its task to accomplish which is then passed to the next layer.   Input
                                                                                                                 Output
                          i.   The first layer of a Neural Network is known as the input
                             layer. The job of an input layer is to acquire data and feed
                             it to the Neural Network. No processing occurs at the input
                             layer.
                          ii.   Next to it, are the hidden layers. Hidden layers are the
                             layers in which the whole processing occurs.

                          iii.   The last hidden layer passes the final processed data to
                             the output layer which then gives it to the user as the final
                             output.

                 C.   Competency-based/Application-based questions:
                       The following questions consist of two statements: Assertion (A) and Reason (R).
                     1.  Assertion (A): Deep Learning is a subset of machine learning.

                        Reason (R): AI includes both ML and DL.
                     2.  Assertion (A): Supervised Learning makes use of unlabelled data.
                        Reason (R): Any ML technique revolves around training the AI model with data.
                     3.  Assertion (A): Machine Learning is an automated process.

                        Reason (R): The algorithm automatically frames the rules from the data.
                     Answer these questions selecting the appropriate option given below:
                        a. Both A and R are true and R is the correct explanation of A
                        b. Both A and R are true and R is not the correct explanation of A



                                                                                            Introduction to AI  127
   124   125   126   127   128   129   130   131   132   133   134