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Artificial Intelligence helps computers to understand and recognise patterns, much like how humans do. Just as
              we learn to recognise patterns in different things we see or hear, AI can do the same with various types of data,
              whether it’s numbers, images, or even speech and text. These patterns help AI to solve puzzles – like identifying
              dogs and muffins, or predicting floods, earthquakes, etc.


                              Task                                             #Problem Solving & Logical Reasoning



                  Let’s think and answer a few more!
                  1.  Find out the middle value from the given numbers?
                     2, 5, 8, 11, 14


                  2.   Fill in the missing number and find the next number in the given series?
                     3, 6, 12, 24, ___, 96, ___


                  3.  Identify the highest temperature in the given graph and mention the time of it.
                                                          Temperature v/s Time

                                                  Temperature  50
                                                     45
                                                     40
                                                     35
                                                     30

                                                         6 am  10 am  2 am  6 am
                                                                  Time

                  4.  How many faces are there in a dice?

                  5.  How many sides does a coin have?

                  6.  What is the shape of a ball?




                       Essential Mathematics for AI

              Math will help us to better understand AI and its way of working, but what kind of math is needed for AI?
              The kind of math needed for AI includes:
              1.   Probability and Statistics (exploring data): Probability theory and statistics are key fundamentals for many
                  AI algorithms, particularly those involving machine learning. It is useful in tasks such as natural language
                  processing, computer vision, and decision-making.
              2.   Linear Algebra (finding out unknown or missing values): Linear algebra is involved in large-scale data
                  processing playing a vital role in machine learning and AI. It performs operations in neural networks, image
                  processing, and data transformations.
              3.   Calculus (training and improving AI model): Calculus is essential for understanding the best possible
                  solution algorithms used in machine learning. It minimise mistakes and maximise the parameters of machine
                  learning models.
              4.  Graph Theory: Graph theory is used in AI representing trends using data visualisation.

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