West Bengal Board Class 11 Applied Artificial Intelligence Syllabus 2024-25: Download Syllabus PDF Here!

To impart knowledge about basic computer fundamentals and Python programming required for implementing Artificial Intelligence (AI) and Machine Learning (ML) applications. 

To enable the students to understand the history of AI and the basic principles of modern AI. 

To enable the students to understand the basics of machine learning(ML), Artificial Neural Networks, and deep learning(DL).

To enable the students to understand the uses of AI and ML/DL in various applications including Natural Language Processing(NLP), speech recognition, Image Processing & Computer Vision, weather Predictions, Medicine and Health care, Economics, eCommerce, Government law and policy-making, environmental sustainability, Chatbots and ChatGPT.

To enable the student to understand ethics in AI.

 To gain practical experience in handling various AI and ML tools and implementing real world applications using those tools.  

Semester 1 

Unit-1 Computer Fundamental

Classification of computers: Micro, mini, mainframe and supercomputers Computer architecture: (Block diagram-based): important units like CPU, Memory, Input and output units of a computer, interaction of computer units via system bus. Data flow between CPU, Memory, and I/O devices. Different parts of the CPU and their functions (in brief). Types of memory (examples). Cache memory. Information transfer from Memory to Processor (a block diagram with a brief description). Mention of different types of I/O devices with examples, Processor to I/O Devices communication (a block diagram with brief description). 

Number systems and Logic gates: How a computer manipulates or stores numbers. Decimal number system, conversion of decimal to binary, octal, and Hexadecimal number system. Logic gates - basic logic gates: AND gate, OR gate, NOT gate, and XOR gate. Laws of Boolean algebra. An example of a small logic circuit containing AND gates, OR gates, and/or NOT gates.  

Computer Network: definition, various types of networks -LAN, WAN, Internet (brief introduction with suitable figures). Very short introduction (with diagrams/figures) to network devices- Network Interface Card, Hub, Repeater, Switch, Bridge, Router, Gateway. 

Software: Difference between software and hardware, Classification of Software with examples. Basic Concepts of Operating Systems (OS)- functions of OS, Types of OS, Windows operating system desktop, icons, menu, taskbar. File System- creating files/folders, deleting files/folders, copying files/folders from one drive to another. 

Unit-2 Software & Languages

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 languages (e.g., Python)), Overview of procedural and object-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)).Concept of Algorithm and Flowchart. Basic programming concepts, What is programming language? Classification of programming languages with examples. What is a computer program?

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, different types of expressions, input and output built-in functions, Python comment, Lists -accessing list element, updating list, deleting list, List vs tuple. Control structures- conditional statements with small examples, While loop, For loop. Arrays-searching in an array (a simple example). Defining user-defined functions(with simple examples). Some important Python libraries-Numpy, OpenCV, Matplotlib, NLTK, Pandas((very short description for each library).  

Semester 2

Unit-4 Foundation of AI

History of AI, What is natural intelligence? What is Artificial Intelligence(AI)? Strong AI vs. weak AI. 

AI agent, An architecture of an AI agent( a block diagram and short description of each component). 

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- problem understanding, data acquisition, Features, Data representation, modeling using approaches like rule-based, supervised learning, unsupervised learning, and Reinforcement Learning(short description of each modeling approach with simple examples).  

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 (in detail).

Decision tree classifier (basic idea only, no induction algorithm). 

Unit-6 Concept of Unsupervised Learning

K-means clustering algorithm. Illustration with an example.

Unit-7 Preliminary Concept of Artificial Neural Network

Neural Network- biological motivation, comparison between Artificial Neuron and Human Neuron. 

 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 (only a diagram showing interconnections among neurons at multiple layers). 

 High-level description of Forward pass and backward pass of the backpropagation (BP) algorithm used for training. 

Multilayer feedforward neural network (No mathematical derivation). 

 How is deep learning (DL) related to Artificial Neural Networks? Difference between shallow and deep learning.