West Bengal Board Class 11 Applied Artificial Intelligence Syllabus 2024-25: A Comprehensive Guide

The West Bengal Council of Higher Secondary Education has released the syllabus for Class 11 students, and it’s packed with exciting topics in Applied Artificial Intelligence (APAI). In this article, we’ll delve into the course objectives, syllabus, and what students can expect from this innovative program.

Course Objectives

The APAI course aims to develop proficiency in Class 11 students for Artificial Intelligence (AI), focusing on the basic principles of AI and ethics in AI. Students will gain practical experience in handling various AI tools, making them industry-ready.

Syllabus

The syllabus is divided into two semesters, covering a range of topics that will give students a solid foundation in AI and programming.

Semester 1

Unit-1 Computer Fundamental
- Classification of computers: Micro, mini, mainframe, and supercomputers
- Computer architecture: CPU, Memory, Input, and output units of a computer
- Data flow between CPU, Memory, and I/O devices
- Types of memory and cache memory
- I/O devices and their communication with the processor

Computer architecture is the backbone of AI systems.

Unit-2 Software & Languages
- Basics of Computer Programming: high-level language, assembly language, and machine language
- Overview of Compiler and Interpreter
- Procedural and object-oriented programming
- Concept of Algorithm and Flowchart
- Basic programming concepts and classification of programming languages

Programming languages are essential for AI development.

Unit-3 Python Programming
- Features of Python programming language
- Applications of Python
- Installing Jupyter using Anaconda
- Steps to open Python Shell in interactive mode
- Steps to create Python file
- Variables, data types, operators, and control structures
- Lists, arrays, and searching in an array
- Defining user-defined functions
- Important Python libraries: Numpy, OpenCV, Matplotlib, NLTK, and Pandas

Python is a popular language used in AI and ML.

Semester 2

Unit-4 Foundation of AI
- History of AI
- What is natural intelligence? What is Artificial Intelligence (AI)?
- AI agent and its architecture
- Relationships between AI, Machine Learning (ML), and Deep Learning (DL)
- What is Machine Learning? Difference between traditional programming and Machine Learning
- Different types of Machine Learning
- Advantages of ML over DL
- Basic steps of ML system design

AI agents are the building blocks of intelligent systems.

Unit-5 Concept of Supervised Learning
- Supervised learning - a block diagram with a short description
- Regression and classification with simple examples
- Common supervised classifiers - K-Nearest Neighbour search algorithm
- Decision tree classifier

Supervised learning is a fundamental concept in ML.

Unit-6 Concept of Unsupervised Learning
- K-means clustering algorithm
- Illustration with an example

Unsupervised learning helps in discovering patterns in data.

Unit-7 Preliminary Concept of Artificial Neural Network
- Neural Network - biological motivation
- Artificial Neuron as a processing unit
- Perceptron learning rule for updating weights of an artificial neuron
- Limitation of a perceptron in solving XOR problem
- Multilayer feedforward neural network
- High-level description of Forward pass and backward pass of the backpropagation (BP) algorithm used for training
- Difference between shallow and deep learning

Neural networks are the backbone of Deep Learning.

The West Bengal Board Class 11 Applied Artificial Intelligence Syllabus 2024-25 is a comprehensive program that will equip students with the skills and knowledge required to succeed in the AI industry. With a focus on practical experience and theoretical foundations, this course is an excellent opportunity for students to explore the exciting world of AI.