B.Tech in Artificial Intelligence Syllabus Semester-wise Guide

B.Tech in Artificial Intelligence Syllabus: Semester-wise Breakdown

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Ankit Krishnan

2026-09-13T13:10:10.578+05:30

The B.Tech Artificial Intelligence program comprises study material for neural networks, natural language processing, computer vision, and latest advances in generative artificial intelligence.

Overview

The course structure of B.Tech Artificial Intelligence is based on deep learning models, reinforcement learning algorithms, and generative AI models for eight semesters in accordance with the standards laid down by AICTE. The students first gain knowledge about basic concepts in machine learning before proceeding onto complex machine learning techniques such as transformers and multimodal AI.

Furthermore, students will be able to grasp the knowledge of optimization techniques in neural networks, natural language processing, computer vision, reinforcement learning, ethical AI, edge AI deployment techniques, and federated learning from practical sessions involving generative adversarial networks, engineering of autonomous systems, and AI trends.

B.Tech in Artificial Intelligence: Course Fees, Eligibility, Admission 2026, Career Prospects
B.Tech Artificial Intelligence Course: Core Subjects, Electives, Entrance Exams
B.Tech Artificial Intelligence Admission 2026
B.Tech Artificial Intelligence Fees in India

B.Tech in Artificial Intelligence Syllabus: Semester-wise Breakdown

The program offers complete training in artificial intelligence, from machine learning algorithms to deep learning systems and intelligent systems design. While exploring such evolving programs, many students also check a college admission app to quickly compare course content and see what different institutes are actually offering—it just makes things a bit clearer. The course material includes topics on current developments in large language models, diffusion models, and neuro-symbolic AI necessary for the development of future cognitive computing technologies.

Syllabus for B.Tech in Artificial Intelligence: Semester 1

The foundational sciences take center stage in the first semester of the B.Tech Artificial Intelligence program. Students learn about the fundamentals of computational intelligence through various courses. They learn mathematics, programming fundamentals, and physics applications aligned with AI industry requirements.

Subject	Topics Covered
Engineering Mathematics I	Matrix algebra operations, vector calculus applications, differential equations solutions, probability distributions analysis, and linear transformations
Programming Fundamentals	Python programming basics, data types manipulation, control structures implementation, functions development, and object-oriented programming concepts
Physics for Engineers	Mechanics principles application, wave propagation analysis, electromagnetic theory fundamentals, quantum mechanics basics, and computational physics
Digital Logic Design	Boolean algebra systems, logic gate implementation, combinational circuits design, sequential circuits analysis, and computer arithmetic operations
Communication Skills	Technical writing proficiency, presentation delivery techniques, group discussion practice, professional communication standards, and documentation practices
Environmental Science	Sustainable computing techniques, Green AI movements, energy-efficient data centres, carbon footprint assessment, and ethical use of technology

B.Tech in Artificial Intelligence Syllabus: Semester 2

AI Sem 2 B.Tech is all about computer basics. The students learn about mathematical principles via special classes on AI. Some of the subjects covered are algorithms, data structures, optimization, and discrete mathematics.

Subject	Topics Covered
Engineering Mathematics II	Applications in discrete mathematics, usage of graph theory, applications in combinatorics, developments in probability theory, and statistical inference techniques
Data Structures & Algorithms	Array operations optimisation, linked list implementation, tree traversals efficiency, graph algorithms development, and complexity analysis techniques
Object-Oriented Programming	Java C++ programming mastery, inheritance mechanisms implementation, polymorphism applications, exception handling strategies, and design patterns usage
Database Management Systems	Relational database design, SQL query optimisation, NoSQL databases implementation, data normalisation techniques, and transaction management protocols
Computer Organisation	CPU architecture design, memory hierarchy optimisation, pipelining techniques implementation, cache coherence protocols, and parallel processing concepts
Statistics for AI	Descriptive inferential statistics, hypothesis testing frameworks, regression analysis techniques, Bayesian inference methods, and data distribution modelling

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B.Tech in Artificial Intelligence Syllabus: Semester 3

The B.Tech Artificial Intelligence Third Semester provides effective exposure to different machine learning fields. This course provides thorough exposure to supervised learning approaches and the basics of neural networks.

Subject	Topics Covered
Machine Learning Fundamentals	Models for linear regression, algorithms for logistic regression, decision trees modeling, ensemble modeling, and validation techniques for models.
Artificial Intelligence	Search algorithms optimisation, knowledge representation schemes, logic programming implementation, game playing strategies, and AI planning techniques
Operating Systems	Process scheduling algorithms, memory management strategies, file systems organisation, concurrency control mechanisms, and system performance analysis
Software Engineering	Requirements analysis methodologies, system design patterns implementation, agile development practices, testing frameworks development, and DevOps pipelines
Signals & Systems	Signal processing fundamentals, Fourier transforms application, digital signal processing, convolutional operations, and time-frequency analysis
AI Programming Lab	Python scientific computing, NumPy, pandas mastery, data visualisation techniques, scikit-learn implementation, and Jupyter notebook workflows

B.Tech in Artificial Intelligence Syllabus: Semester 4

B.Tech Artificial Intelligence Semester 4 equips students with advanced knowledge in deep learning techniques. The course focuses on neural architecture and computer vision techniques. Students acquire advanced knowledge in convolutional networks, a key requirement for developers of intelligent systems.

Subject	Topics Covered
Deep Learning	Neural network architectures, backpropagation algorithms, convolutional networks implementation, recurrent networks design, and optimisation techniques
Computer Vision	Image processing techniques, feature extraction methods, object detection algorithms, image segmentation frameworks, and visual recognition systems
Natural Language Processing	Text preprocessing pipelines, language models development, sequence labelling techniques, sentiment analysis implementation, and text generation models
Data Mining	Association rule mining, clustering algorithms implementation, anomaly detection techniques, dimensionality reduction methods, and pattern discovery
Theory of Computation	Automata theory applications, formal language processing, computability concepts exploration, complexity classes analysis, and algorithm design paradigms
Deep Learning Lab	TensorFlow PyTorch frameworks, CNN RNN implementation, transfer learning techniques, hyperparameter tuning strategies, and model deployment pipelines

B.Tech in Artificial Intelligence Syllabus: Semester 5

B.Tech Artificial Intelligence (AI) Semester 5 involves the practical usage of AI through proper utilization of strategies. The course emphasizes reinforcement learning and generative models. The student becomes an expert in creating autonomous systems which enable him to become a research leader.

Subject	Topics Covered
Reinforcement Learning	Markov decision process theory, Q-learning, policy gradients, multi-agent system modeling, deep reinforcement learning, and techniques for exploration
Generative AI	Generative adversarial networks, variational autoencoders, diffusion models implementation, transformer architectures, and multimodal generation systems
Big Data Analytics	Apache Hadoop Spark ecosystems, distributed computing tools, real-time streaming processing, graph processing systems, and machine learning pipelines
AI Ethics & Governance	Algorithmic fairness, metrics for assessing algorithmic bias, explainability techniques, privacy-preserving machine learning, and responsible AI deployment frameworks
Cloud Computing for AI	Amazon SageMaker, Azure Machine Learning, GPU cloud instances, model serving, machine learning operationalization pipelines, and scalable AI deployments
Elective I	Robotics, Speech Processing, Knowledge Graphs, Time Series Analysis, or AI for Healthcare

B.Tech in Artificial Intelligence Syllabus: Semester 6

The B.Tech Artificial Intelligence 6th semester makes use of sophisticated intelligent systems. The course is completed with industry exposure and innovation emphasis. Students will acquire knowledge through projects and specialization, which are critical for any AI career.

Subject	Topics Covered
Advanced Deep Learning	Implementing transformer models, mastering attention mechanisms, fine-tuning large language models, developing vision transformers, and building multimodal AI systemss
AI Systems Engineering	Model deployment strategies, microservices architecture design, container orchestration with Kubernetes, CI/CD pipelines automation, and production monitoring systems
Edge AI & IoT	Implementing TinyML, optimizing edge devices, implementing federated learning, real-time inference pipelines, and building embedded AI systems
Human-Computer Interaction	User experience design principles, conversational AI interfaces, augmented reality interactions, accessibility standards implementation, and usability testing
Elective II	Quantum Machine Learning, Neuro-symbolic AI, AI Security, Autonomous Vehicles, or Financial AI
Industry Project	Real-world problem formulation, dataset curation pipeline development, model training optimisation, deployment infrastructure setup, and performance evaluation

Frequently Asked Questions

How does B. Can Tech AI incorporate transformer architectures?

Semester 6 Advanced Deep Learning covers attention mechanisms, large language models, and multimodal transformers.

What generative AI models feature in the AI curriculum?

Courses teach GANs, diffusion models, variational autoencoders, and transformer-based generation systems.

Which deep learning frameworks does the programme teach?

Semesters 3-6 cover TensorFlow, PyTorch, JAX, Keras, and the Hugging Face Transformers library.

Which AI ethics classes are covered by the curriculum?

The students get to study the subjects of bias mitigation, explainable AI, privacy-preserving machine learning, and ethical deployment in Semester 5.

What are some of the edge AI topics covered by the curriculum?

Students learn about TinyML, federated learning, optimization of edge devices, and real-time inference engines in Semester 6.

B.Tech in Artificial Intelligence Semester-wise Syllabus

B.Tech in Artificial Intelligence Syllabus: Semester-wise Breakdown

Overview

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B.Tech in Artificial Intelligence Syllabus: Semester-wise Breakdown

Syllabus for B.Tech in Artificial Intelligence: Semester 1

B.Tech in Artificial Intelligence Syllabus: Semester 2

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Indian Institute of Technology BHU Varanasi

Indian Institute of Technology (IIT) BHU Varanasi

Indian Institute of Technology BHU Varanasi

Indian Institute of Technology BHU Varanasi

Indian Institute of Technology (IIT) BHU Varanasi

Indian Institute of Technology BHU Varanasi

Indian Institute of Technology, BHU Varanasi

B.Tech in Artificial Intelligence Syllabus: Semester 3

B.Tech in Artificial Intelligence Syllabus: Semester 4

Also Read

B.Tech in Artificial Intelligence Syllabus: Semester 5

B.Tech in Artificial Intelligence Syllabus: Semester 6

Frequently Asked Questions

How does B. Can Tech AI incorporate transformer architectures?

What generative AI models feature in the AI curriculum?

Which deep learning frameworks does the programme teach?

Which AI ethics classes are covered by the curriculum?

What are some of the edge AI topics covered by the curriculum?