Artificial Intelligence Syllabus

The syllabus differs across B.Tech, BSc, BCA and postgraduate programmes. The following curriculum is representative.

First-Year Subjects

Engineering Mathematics: Calculus, linear algebra, differential equations and numerical methods.

Probability and Statistics: Random variables, distributions, estimation and hypothesis testing.

Programming Fundamentals: Variables, control structures, functions, data structures and debugging.

Python Programming: Syntax, libraries, object-oriented programming and scientific computing.

C or C++ Programming: Memory, pointers, object-oriented concepts and efficient implementation.

Digital Logic: Boolean algebra, logic circuits, combinational systems and sequential systems.

Computer Organisation: Processors, memory, instruction execution and input-output systems.

Engineering Physics: Scientific concepts relevant to engineering systems.

Communication Skills: Technical writing, presentations and teamwork.

Second-Year Subjects

Data Structures: Arrays, lists, stacks, queues, trees, graphs and hashing.

Algorithms: Complexity, searching, sorting, recursion, greedy methods and dynamic programming.

Discrete Mathematics: Logic, sets, relations, combinatorics, graphs and proofs.

Database Management Systems: Relational databases, SQL, schema design and transactions.

Operating Systems: Processes, memory, file systems, scheduling and concurrency.

Computer Networks: Communication models, protocols, routing and network applications.

Object-Oriented Programming: Classes, inheritance, abstraction and software organisation.

Optimisation: Objective functions, constraints, gradient methods and mathematical optimisation.

Software Engineering: Requirements, design, testing, version control and development processes.

Third-Year Subjects

Introduction to Artificial Intelligence: Intelligent agents, search, logic, reasoning, planning and uncertainty.

Machine Learning: Regression, classification, decision trees, support vector machines, clustering and evaluation.

Deep Learning: Neural networks, optimisation, regularisation and deep architectures.

Natural Language Processing: Text representation, language modelling, classification, translation and generation.

Computer Vision: Image processing, feature extraction, classification, detection and segmentation.

Data Mining: Pattern discovery, association, clustering and anomaly detection.

Big Data Systems: Distributed storage, parallel processing and large-scale data pipelines.

Cloud Computing: Virtualisation, services, deployment and scalable computing.

AI Ethics: Fairness, bias, privacy, accountability, safety and social impact.

AI Laboratory: Implementation and comparison of algorithms using real datasets.

Final-Year Subjects

Reinforcement Learning: Markov decision processes, value methods, policy methods and exploration.

Generative AI: Generative models, transformers, foundation models and evaluation.

MLOps: Data and model pipelines, deployment, monitoring, versioning and governance.

Explainable AI: Interpretation, feature importance, model explanation and communication of uncertainty.

AI Security: Adversarial attacks, model abuse, data poisoning, privacy and system protection.

Edge AI: Efficient AI deployment on mobile, embedded and resource-constrained devices.

AI Product Development: User needs, experimentation, metrics, risk and lifecycle management.

Internship: Professional experience in AI, software, data or research.

Major Project: A complete problem involving data, model development, evaluation, deployment and documentation.

Representative Semester-Wise B.Tech AI Syllabus

Artificial Intelligence Core Subjects Explained

Linear Algebra: Vectors, matrices, eigenvalues and matrix decomposition are essential for machine learning.

Calculus: Derivatives and gradients support optimisation and neural-network training.

Probability: AI models estimate uncertainty and relationships using probability.

Statistics: Statistics supports data analysis, estimation, experimentation and model evaluation.

Data Structures and Algorithms: Efficient software depends on selecting suitable computational structures.

Machine Learning: Students learn how models generalise from training examples.

Deep Learning: Students study neural networks capable of learning complex representations.

NLP: Students learn to represent, analyse and generate human language.

Computer Vision: Students learn to interpret visual information.

MLOps: Students learn how models move from experiments into reliable production systems.

Artificial Intelligence Electives

Possible electives include:

• advanced machine learning;
• probabilistic graphical models;
• information retrieval;
• speech processing;
• recommender systems;
• graph machine learning;
• autonomous systems;
• robotics;
• healthcare AI;
• financial AI;
• AI for cyber security;
• computational neuroscience;
• multi-agent systems;
• causal inference;
• time-series forecasting;
• federated learning;
• privacy-preserving machine learning;
• AI governance;
• foundation models;
• multimodal AI;
• synthetic data;
• quantum machine learning; and
• human-computer interaction.

Artificial Intelligence Laboratories

Programming Laboratory: Implementation of algorithms and software fundamentals.

Data Science Laboratory: Data cleaning, visualisation and statistical analysis.

Machine Learning Laboratory: Training, validation and comparison of predictive models.

Deep Learning Laboratory: Neural-network implementation using modern frameworks.

NLP Laboratory: Text preprocessing, classification, embeddings and language models.

Computer Vision Laboratory: Image processing, object recognition and segmentation.

Cloud and MLOps Laboratory: Deployment, APIs, containers, pipelines and monitoring.

Responsible AI Laboratory: Bias analysis, explainability, privacy and risk testing.

Artificial Intelligence Project Ideas

Students may build:

• crop-disease detection;
• document classification;
• multilingual question-answering;
• medical-image screening support;
• retail demand forecasting;
• fraud-detection system;
• traffic-sign recognition;
• industrial defect detection;
• assistive reading tool;
• personalised learning system;
• recommendation engine;
• fake-review detection;
• waste-classification model;
• energy-demand forecasting;
• speech-to-text application;
• legal-document search;
• disaster-information assistant;
• predictive-maintenance system;
• explainable loan-risk model;
• accessible image-description system;
• misinformation analysis tool;
• privacy-preserving learning experiment;
• small language-model application;
• model-monitoring dashboard; or
• AI-assisted cyber-security alert system.

Projects involving healthcare, finance, hiring or other high-impact decisions should include strong ethical, privacy and evaluation safeguards.

How to Evaluate an AI Project

A technically responsible AI project should explain:

• the problem;
• intended users;
• data source;
• consent and licensing;
• preprocessing;
• baseline;
• model choice;
• evaluation metrics;
• error analysis;
• fairness;
• privacy;
• security;
• limitations;
• deployment plan; and
• monitoring.

High accuracy alone does not prove that a system is useful or safe.