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      Social Network analysis: Finding groups or communities of people with similar interests or
                         connections. It helps in understanding relationships and improving user recommendations.

                         Unlabelled Input data       Model Analysis            Clustering           Clusters / Groupings
                                                (Detects Hidden Patterns)

                                                The model explores the data
                                                  and finds similarities and                     The model forms groups (clusters)
                                                      relationships.     Similar footballs are grouped   based on patterns and similarities,
                                                                           together into clusters.
                                                                                                    without using any labels.

                      The diagram shows unsupervised learning using different footballs as unlabelled data. The model
                      studies the footballs and finds similarities such as colour, size and design. Without any given labels,
                      it learns patterns on its own. It then groups similar footballs into clusters. When new footballs are
                      added, the model places them into the most suitable group based on learned patterns.

                      Reinforcement Learning
                      Reinforcement  Learning  (RL) is  a  type  of  machine  learning  in  which systems  learn through
                      trial and error. Instead of being given correct answers, the machine explores different actions

                      and  improves its  performance  by  receiving  rewards  for correct  decisions  and  penalties  for
                      incorrect ones.

                      Over time, it focuses on actions that give the highest rewards and develops better strategies.
                      This approach is commonly used in areas like robotics, automation and smart decision-making
                      systems.
                      For example, an online recommendation system learns by suggesting different products or videos
                      to users without knowing their exact preferences initially. When users click or like a suggestion,

                      it receives a reward, while ignoring the suggestion acts as a penalty. Over time, it improves its
                      recommendations by learning from user responses.

                       ethical minds

                        AI systems, which rely on algorithmic thinking to process data and make decisions, can be biased if
                        they are trained on biased data.

                      Some applications of reinforcement learning are as follows:
                        Game-playing AI: AI systems learn to play games like chess, Go or video games by trying
                         different moves and strategies. They receive rewards for winning or making good moves and
                         penalties for mistakes, which helps them improve over time.

                       24        Artificial Intelligence (CT & AI)-VI

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