Academic Programs

AI & ML / Academic Programs

Programs Offered

The Department of Artificial Intelligence & Machine Learning offers a four-year B.Tech in AI & ML — a JNTUK-affiliated program designed to produce industry-ready engineers fluent in modern machine learning, deep learning, and AI engineering practices. The curriculum is augmented by elective Specialization Tracks that let students go deep into Computer Vision, NLP, Deep Learning & MLOps, or Generative AI.

Our Programs

B.Tech AI & ML Undergraduate
B.Tech AI & Machine Learning
Specialization Tracks Electives
Specialization Tracks

B.Tech — Artificial Intelligence & Machine Learning

The Program Educational Objectives (PEOs) describe the career and professional accomplishments our B.Tech AI & ML graduates are expected to attain within a few years of graduation. They guide curriculum design and align the program with industry, research, and societal expectations.

  • Graduates will apply foundational mathematics, statistics, and computer science to design and deploy AI and machine learning solutions to real-world problems.
  • Graduates will engineer modern ML systems — including deep learning, NLP, computer vision and generative AI — with attention to scalability, reliability and responsible use.
  • Graduates will demonstrate professional ethics, life-long learning, and leadership across diverse industry, research, and entrepreneurial roles.

Program Outcomes (POs) describe the knowledge, skills and attributes graduates will possess at the time of graduation. They are aligned with national accreditation standards and ensure our graduates are well-prepared for professional practice in AI & ML.

  • Engineering Knowledge: Apply mathematics, statistics, computer science, and AI/ML fundamentals to formulate and solve complex problems.
  • Problem Analysis: Identify, formulate and analyse problems in data, perception, language and decision-making using first principles.
  • Design & Development: Design ML pipelines, deep learning architectures and intelligent systems that meet specified needs with appropriate constraints.
  • Investigation: Conduct experiments, analyse datasets, interpret model behaviour and synthesize valid conclusions.
  • Modern Tools: Gain hands-on proficiency with PyTorch, TensorFlow, Hugging Face, scikit-learn, cloud platforms, and MLOps tooling.
  • Engineer & Society: Assess societal, legal, and cultural implications of deploying AI in real-world contexts.
  • Sustainability: Consider the environmental and energy implications of large-scale AI systems.
  • Ethics: Apply ethical principles around fairness, accountability, transparency, privacy and responsible AI.
  • Teamwork: Function effectively in multidisciplinary teams spanning data, product, and engineering roles.
  • Communication: Communicate models, experiments and trade-offs clearly to technical and non-technical audiences.
  • Project Management: Apply engineering and management principles to lead ML projects from ideation through deployment.
  • Lifelong Learning: Engage in independent learning in a rapidly evolving AI/ML landscape.

Program Specific Outcomes (PSOs) capture specialised AI/ML competencies that distinguish our graduates. They focus on the technical and professional capabilities developed through coursework, lab work and capstone projects.

  • Build, evaluate and deploy machine learning and deep learning models for vision, language, recommendation, forecasting and decision-making problems.
  • Engineer end-to-end ML systems — data pipelines, training, evaluation, MLOps, monitoring — using industry-standard frameworks and cloud platforms.
  • Apply responsible AI practices to ensure fairness, robustness, privacy and accountability across the model lifecycle.

The B.Tech AI & ML curriculum balances mathematical foundations, core computer science, and applied AI/ML coursework spanning the full modern stack.

  • Linear Algebra, Probability & Statistics, Optimization
  • Core Computer Science — Data Structures, Algorithms, DBMS, OS, Networks
  • Artificial Intelligence & Machine Learning
  • Deep Learning
  • Natural Language Processing
  • Computer Vision
  • Big Data Analytics & Data Visualization
  • Cloud Computing & Internet of Things (IoT)
  • Generative AI & Prompt Engineering
  • Ethical & Responsible AI
  • DevOps & MLOps

Specialization Tracks

Build vision systems that perceive and interpret the world — from image classification and detection to segmentation, 3D vision, and multimodal models.

  • Image Classification, Object Detection, Segmentation
  • Convolutional Neural Networks & Vision Transformers
  • Video Understanding & Action Recognition
  • 3D Vision, Depth Estimation, and Visual SLAM (overview)
  • Edge Deployment with ONNX, TensorRT, and mobile runtimes

Design language systems that understand, generate, and reason — from classical text processing to modern transformer-based and retrieval-augmented architectures.

  • Text Preprocessing, Embeddings & Classical NLP
  • Sequence Models, Transformers, and Attention
  • Large Language Models, Fine-tuning & LoRA
  • Retrieval-Augmented Generation (RAG) & Vector Databases
  • Conversational AI & Dialog Systems

Go beyond model training to engineer reliable ML systems — covering architectures, training infrastructure, deployment, monitoring and continuous delivery for ML.

  • Advanced Neural Architectures & Optimization
  • Distributed Training on GPUs & Mixed Precision
  • Experiment Tracking, Model Registries & Feature Stores
  • CI/CD for ML, Containerization & Kubernetes
  • Model Monitoring, Drift Detection & Observability

Master the modern generative stack — foundation models, diffusion, multimodal systems, agentic applications, and responsible deployment.

  • Foundations of Generative Models — VAEs, GANs, Diffusion
  • Foundation Models, Prompt Engineering & Tool Use
  • Multimodal Models — Text, Image, Audio
  • Agentic AI, Planning & Function Calling
  • Safety, Evaluation & Responsible Deployment
AI & ML Programs
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