This course covers the entite syllabus of grade 9 portion. After the completion of syllabus through board paper discussion is done and startegies are discussed to do well in Exam. 

SYLLABUS-

PART I
ROBOTICS
1. Introduction to Robotics
(i) Understanding Robots.
Basic understanding of what a robot is;
definition and characteristics; benefits of
using robots (with respect to humans):
increased quality, increased productivity,
increased efficiency, longer working span,
working in hazardous environments,
improved workplace. Limitations of using
robots.
(ii) Evolution of Robots; Laws of Robotics.
Brief history of Robots with respect to their
evolution from 1900’s till date. Definition of
Robotics, the three Laws of Robotics by Isaac
Asimov (statements only).
(iii) Classification of Robots.
Classification of Robots as: field/terrain
based (arial, ground, underwater) and control
based (manual, automatic): Meaning and
examples of each. Bio-inspired robots:
meaning, purpose and examples (humanoids,
birds, snakes and insects).
(iv) Real world Robots and their applications.
Application of robots in different fields
(domestic, industry, medical, defense,
entertainment and agriculture) with at least
one example of each.
2. Robot as a System
(i) Building blocks of Robots.
General block diagram of a robot. A detailed
study of the building blocks of a robot.
Concept of a robot as having mechanical,
electronic and computational blocks;
functioning and working principle of each
block. Design aspects using examples of
humanoid, aerial, underwater and mobile
robots.
(ii) Identification of Robots.
Identification of robots (through
demonstration/ video/graphic details).
Illustration using an industrial robot (e.g.,
Industrial Robotic Arm), humanoid and
mobile robot. The idea that a mechanical body
can be of any form must be emphasized.
3. Concepts in Robotics
(i) Types of motion; motion in one-dimension
and two-dimension; types of joints and links.
Types of motion (linear, angular, and
circular); a brief understanding of motion in
one-dimension and two-dimension; types of
joints (prismatic, revolute, and spherical);
types of links (rigid and soft). Relevant
examples for each of the above.
(ii) Using links and joints to create specific
motion.
A detailed study of how links and joints
help create specific motion. Identification
of links and joints used in a given system.
Examples for the demonstration can
include Industrial Robot Arms.
(iii) Degree of freedom of a robot
Definition; identification through
illustration.
2PART II
ARTIFICIAL INTELLIGENCE (AI)
1. Introduction to Artificial Intelligence (AI)
(i) Meaning and brief history.
Definition of Artificial Intelligence; brief
account of the history of AI since the time
John McCarthy first coined the term in 1956;
Turing Test, its use and importance.
(ii) Applications and Benefits of AI.
Applications of AI in different fields:
commercial, industry, medical/health care,
defense, banking, entertainment, transport,
security and agriculture. Commonly used AI
applications in daily life such as, online
shopping, search engines, chatbots, voice
assistants, entertainment portals, facial
recognition, driver assisting vehicles,
augmented/ virtual reality.
Benefits of using AI - Automation, smart
decision making, assisting humans, remote
patient monitoring & monitoring the
progression of contagious diseases, analysis
of data for research and development,
efficiently solving complex problems, speedy
disaster recovery strategy, performing
recurring business tasks, reducing the
chances of manual errors, ensuring 24-hour
service availability with the same
performance and consistency throughout the
day.
(iii) Ethical considerations in AI.
A brief understanding of ethics in artificial
intelligence including bias, prejudice,
fairness, accountability, transparency,
interpretability and explainability.
2. Role of Data and Information, Evolution of
Computing
(i) Data and Information: Types of Data (audio,
visual, numeric, text); Data to Information.
Understanding that data is pivotal to Artificial
Intelligence. A brief introduction to how
relevant data is identified, acquired, and
explored, as a precursor to the AI Project
Cycle.
(ii) Evolution of Computing: Pre AI/ML Binary
Logic System, Conditional Gates,
Deterministic computing for deterministic
problems.
An introduction to above mentioned topics,
with the emphasis that earlier computing was
suited for only deterministic problems;
explaining deterministic computing and
deterministic problems giving relevant
examples. Illustrating the limitations of
deterministic computing in solving real life
problems, Comparison between deterministic
and probabilistic nature of real-life problems.
Note: Explanation of how AI can solve a new
class of problems, based on a probabilistic
paradigm. Hence Need for AI: Probabilistic,
real-life problems; The AI Discretion (AI is
not needed for solving deterministic
problems) for example –the difference in
description of temperatures by a machine and
a human. A machine would make a discrete
distinction between cool and hot at a given
temperature for instance if 35°C is hot, then
any temperature 34.9° C and below would be
cool. Humans would, however, describe the
temperature on a range of ‘cool, pleasant,
warm, hot’ and so on based on their subjective
experience of the temperature.
3. Introduction to Data and Programming with
Python
(i) Familiarization with Python.
Introduction to Python and its elementary
concepts: object-oriented, high-level, general
purpose programming language. Uses and
advantages of Python.
(ii) Introduction to data types and variables.
Introduction to a simple python program
structure and the concept of indentation in
Python, different data types in Python -
numeric (int, float), Boolean, sequence type
(tuple, list, strings), sets and dictionary, an
understanding of what kind of data types
should be used in different use cases.

values.
(iii) Introduction to Operators.
Usage of different operators (arithmetic,
logical, assignment, comparison, identity,
membership) on data types, kind of
statements which can be executed in Python.
(iv) Conditional Statements
Introduction to blocks in Python, if
conditions, if else conditions, nested if
conditions, if-else-if (elif) conditional block,
case and switch. Shorthand conditional
statements.
(v) Control Statements.
Meaning and use of loops in python.
Different types of loops (while, for), nested
loops, syntax used. ‘for’ loop for different
types of iterables (list, tuple, string,
dictionary) along with the idea of break,
continue and pass statements, ‘while’ loop
and their use cases.
(vi) Functions
An understanding of both built in and user
defined functions; the importance of
functions to maintain modularity; arguments
given to a function (fixed and variable
length); the concept of default arguments and
return type of a function.
4. AI Concepts and AI Project Framework
(i) AI Concepts
Broad and narrow AI, strong versus weak AI.
Expert systems in AI (for e.g., Eliza).
Computer vision (CV), Natural Language
Processing (NLP) and Neural Network (NN).
(ii) Components and Stages (alias AI Project
Cycle).
Understanding of AI Project Framework,
Stages involved in AI project: Problem
Scoping, Data Acquisition, Data Exploration,
Modelling and Evaluation (brief
understanding of each).