Page 12 - Artificial Intellegence_v2.0_Class_11
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SUBJECT SPECIFIC SKILLS

                 Unit                       Topics                                     Learning Outcomes
                       Introduction-AI for everyone                     Knowledge – Define AI and ML
                          What is AI?                                   Comprehension – What are the AI products/applications in society
                          o   Kids can AI                               and how are they different from non-AI products/applications?
                          History of AI
                          What is Machine Learning?                     Evaluation – What kind of jobs may appear in the future?
                          o     Difference between conventional programming and
                             machine learning
                             How is Machine learning related to AI?
                         o
                            What is data?
                   INTRODUCTION TO AI         Terminology and Related Concepts Intro to  AI
                         o
                             Structured
                             Unstructured
                         o
                         o
                             Examples of unstructured data- text, images
                             Machine learning
                         o
                             Unsupervised learning (examples)
                            o   Supervised learning (examples)
                         o
                          o   Deep learning
                          o   Reinforcement learning
                          o   Machine Learning Techniques and Training
                          o   Neural Networks
                          What machine learning can and cannot do
                           More examples of what machine learning can and cannot do
                          Jobs in AI
                          Key Fields of Application in AI               Knowledge – Where can AI be applied (like in the field of Computer
                          o     Chatbots (Natural Language Processing, speech)  vision, Speech, Text, etc.), What is deep learning?
                                o   Alexa, Siri and others              Comprehension – How AI will impact our society
                   AI APPLICATIONS AND METHODOLOGIES               Characteristics and types of AI
                         o
                             Computer vision
                                                                        Analysis – How should we get ready for the AI age (future)
                         o
                             Weather Predictions
                         o
                             Price forecast for commodities
                             Self-driving cars
                         o
                             Data driven
                         o
                             Autonomous systems
                         o
                             Recommender systems
                         o
                             Human like
                         o
                           Cognitive Computing (Perception, Learning, Reasoning)
                          Recommended deep-dive in NLP, CV, etc.*
                          AI and Society
                          The Future with AI, and AI in Action  (Introduction)
                           Non-technical explanation of deep learning
                          Coursera-ai-for-everyone
                          Introduction to matrices (Recap)              Comprehension – Linear Algebra, Statistics, Basics of Graphs and
                          Introduction to set theory (Recap)            Set theory
                            Simple statistical concepts                 Application – Application of Math in AI
                             Introduction to data table joins
                         o
                   MATHS FOR AI     Visual representation of data, bar graph, histogram,   Synthesis – Representing data in term of mathematical formula
                         frequency bins, scatter plots, etc.
                           With co-ordinates and graphs introduction to dimensionality
                         of data
                          Simple linear equation
                          o   Least square method of regression





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