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UNIT 2: Advance Concepts of Modeling in AI

                     SUB-UNIT                   LEARNING OUTCOMES                    SESSION/ ACTIVITY/ PRACTICAL
               Revisiting AI, ML, DL  Understand AI, ML and DL                Session: Differentiate between AI, ML, and DL
                                                                              Session: Common terminologies used with data
               Modeling              ●  Familiarize with supervised, unsupervised and   Session:  Types  of  AI  Models:  Rule  Based  Approach,
                                      reinforcement learning based approach   Learning Based Approach
                                     ●  Understand subcategories of Supervised,   Session: Categories of Machine learning based models:
                                      Unsupervised and deep learning models   Supervised   Learning   (https://teachablemachine.
                                                                              withgoogle.com/),  Unsupervised  Learning  (https://
                                                                              experiments.withgoogle.com/ai/drum-machine/view/),
                                                                              Reinforcement Learning
                                                                              Session: Subcategories of Supervised Learning Model:
                                                                              Classification Model, Regression Model
                                                                              Session:  Subcategories  of  Unsupervised  Learning
                                                                              Model: Clustering, Association
                                                                              Session:  Subcategories  of  Deep  Learning:  Artificial
                                                                              Neural networks (ANN), Convolutional Neural Network
                                                                              (CNN)
               Artificial Neural Networks  ● Understand Neural Networks       Session: What is Neural Network?
                                     ● Understand how AI makes a decision     Session: How does AI make a Decision?
                                                                              Activity: Human Neural Network – The Game
                                                                              Suggested  Neural  Network  Activity:   https://
                                                                              playground.tensorflow.org/
              UNIT 3: Evaluating Models

                     SUB-UNIT                   LEARNING OUTCOMES                     SESSION/ ACTIVITY/ PRACTICAL
               Importance of Model   Understand  the  role  of  evaluation  in  the  development  Session: What is evaluation?
               Evaluation            and implementation of AI systems.         Session: Need of model evaluation
               Splitting  the  training  set  Understand  Train-test  split  method  for  evaluating  the  Session: Train-test split
               data for Evaluation   performance of a machine learning algorithm
               Accuracy and Error    Understand Accuracy and Error for effectively evaluating  Session: Accuracy
                                     and improving AI models                   Session: Error
                                                                               Activity: Find the accuracy of the AI model
               Evaluation metrics for   Learn about the different types of evaluation techniques  Session: What is Classification?
               classification        in AI, such as Accuracy, Precision, Recall and F1 Score, and   Session: Classification metrics
                                     their significance.
                                                                               Activity: Build the confusion matrix from scratch
                                                                               Activity: Calculate the accuracy of the classifier model
                                                                               Activity:  Decide  the  appropriate  metric  to  evaluate
                                                                               the AI model
               Ethical concerns around   Understand ethical concerns around model evaluation  Session: Bias, Transparency, Accuracy
               model evaluation

              UNIT 4: Statistical Data (To be assessed through Practicals)
                     SUB-UNIT                   LEARNING OUTCOMES                     SESSION/ ACTIVITY/ PRACTICAL
               Introduction & No code AI  Define the concept of Statistical Data and understand its  Session: No code AI tool
               tool                  applications in various fields.           ● Introduction to Data Science & its applications
                                     Define No-Code and Low-Code AI.           ● Meaning of No-Code AI
                                     Identify the differences between Code and No-Code AI   ● No-Code and Low-Code.
                                     concerning Statistical Data.
                                                                               ● Some no-code tools
                                                                               Orange Data Mining Tool:
                                                                               https://orangedatamining.com/download/
                                                                   (x)
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