Page 12 - AI Ver 3.0 class 10_Flipbook
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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)

