West Bengal Board Class 11 Artificial Intelligence Syllabus 2024-25: Download Syllabus PDF For Free!

UNIT NO.

SUBUNIT

TOPICS

MARKS

History of computer, Basic Computer hardware, input and output

devices, Basic computer architecture, input-output devices, memory

and CPU, networking of machines (overview of LAN, MAN, WAN,

Internet, Wifi etc), types of computer (workstation, desktop,

1a

Smartphone, embedded system, etc.), Overview of Software (system

5

software and application software with examples (mention names

Only), Definition of Operating System and functions (mention names

of some popular operating systems like Windows, Linux, Android,

Etc).

Bit, Byte and Word, Number System (Base, Binary, Decimal, Octal,

Unit -1 Computer Fundamental (15)

1b

Hexadecimal), Conversion of number systems, Boolean logic (Boolean Gates ), Boolean operators (OR, AND and NOT), ASCII code, Concept of Algorithm and Flowchart.

5

Basics of Computer Programming (three levels: high-level language,

assembly language, machine language, definition and block

diagrams), Overview of Compiler and Interpreter (definition and

mention name of major compiled (e.g., C, C++) and interpreted

1c

languages (e.g., Python), Overview of procedural and object

5

oriented programming (key features and just the basic differences,

Mention names of some popular procedural (e.g., BASIC,

FORTRAN, C) and object-oriented programming languages (e.g., C++,

Java, Python).

UNIT NO.

SUBUNIT

TOPICS

MARKS

Basics of Python programming (with a simple 'hello world' program,

Process of writing a program, running it, and printing a statement),

Concept of class and object, data types (integer, float, string), Notion

of a variable, Operators (assignment, logical, arithmetic etc.),

2a

accepting input from the console, conditional statements (If else and

5

Nested If else ), Collections (List, Tuple, Sets and Dictionary), Loops

(For Loop, While Loop & Nested Loops), Iterator, String and

Fundamental string operations (compare, concatenation, sub-string

etc.), Function, Recursion.

Unit -2

Introduction to Python Programm-

ing (15)

Overview of linear and non-linear data structure (definition,

schematic view and difference), array (1D, 2D and its relation with matrix, basic operations: access elements using index, insert, delete,

search), stack (concept of LIFO, basic operations: Push, Pop, peek,

2b

size), queue (concept of FIFO, basic operations: Enqueue, Dequeue,

6

peek, size), use of List methods in Python for basic operations on

array, stack and queue, overview of NumPy library and basic array

operations (arrange(), shape(), ndim(), dtype() etc.), binary tree

(definition and schematic view only) .

Linear search and binary search algorithm, sorting algorithm

2c

( bubble sort only)

4

Basic matrix operations like matrix addition, subtraction,

Multiplication, transpose of a matrix, identity matrix. A brief

Unit -3 Introduction to Linear Algebra (5)

3

introduction to vectors, unit vectors, normal vectors, Euclidean space

Probability distribution, frequency, mean, median and mode, variance and standard deviation, Gaussian distribution, Distance function,

5

Euclidean norm, the distance between two points in 2D and 3D and

extension of the idea to n dimensions

UNIT NO.

SUBUNIT

TOPICS

MARKS

Unit -4 Foundation of AI & Search as Optimization (18)

4a

History of AI: Alan Turing and Cracking Enigma, Mark 1 machines, 1956-the birth of the term AI,

AI winter of the ’70s, expert systems of the 1980s, skipped the journey of present-day AI. Distinction between terms AI, Pattern Recognition and Machine Learning

Note: should be taught as a story more than a flow of information about World War 2, Enigma and Alan Turing, and the birth of modern computers.

3

4b

Search as optimization: how to search for the best answer to a question? playing tic-tac-toe

● State Space Search, different states as different solutions to a problem

● Mathematical equation for optimizing a result, for example, tic-tac-toe, the states of the board and equation to calculate the score of the board concerning a player

● Expanding possible states from a state and choosing the best state

Uninformed search

a) Breadth-first search

b) Depth-first search Informed search

a) Heuristic search strategy with tic tac toe example

b) Greedy best-first search

c) A* search - basic idea only( without proof)

d) Hill climbing (only a basic idea with a small example)

e) Simulated Annealing (No algorithm, Only basic idea)

10

4c

Evolution and Darwin's theory, the inspiration of evolutionary algorithms, crossover and mutation,

Russian roulette for random selection, optimization using genetic algorithm, one use of GA (to be chosen) practical: mention libraries and problem.

● Natural evolution theory, survival of the fittest

● Expressing a solution vector as a gene, an example of binary strings

● Crossover and mutation, its equivalent over binary strings

● Random selection of genes from the pool and random mutation

● Fitness function

● Practical example by finding the root of a univariate equation.

5

UNIT NO.

SUBUNIT

TOPICS

MARKS

5

Knowledge representation and reasoning (10)

5

Logic in computer science, propositional logic, logic as expressions, truth table, conjunction, disjunction, syllogism, tautology, De Morgan's theorem. Use of logic to derive conclusions with practical examples [NO LAB COMPONENT]

● Statements as logical propositions

● Atomic and compound propositions

● Negation, conjunction and disjunction as NOT, AND and OR

● Implication and Biconditional statements

● Truth table as a way of proving propositions

● Commutativity and Associativity and Distributive rules

● De Morgan's theorem

● Practical examples to infer meanings from statements

● Simple concept of Unification ( without details of MGU)

● Simple concept of clause (With Simple example)

● Basic concept of Inference (With Simple example)

● Example of Answer Extraction system

● A brief introduction to fuzzy logic (Only a basic idea )

10

6

Uncertainty Management (5)

6

Handling Uncertain Knowledge Uncertainty and Rational Decision Probabilistic Reasoning

Bayes Rule

Conditional probability

Probabilistic inference using Bayes rule

a. General method(Simple cases)

b. Combining evidence

5

7

Preliminary Concept of Chatbots (2)

7

What is Chatbot?

• Examples of different Chatbots

• The flowchart describing the basic working principle of Chatbots.

2