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
(x)

