A typical BE/BTech AIML curriculum in India spans eight semesters and layers computer science foundations with progressively advanced AI/ML theory, tooling, and deployment practice. Early semesters establish the mathematical core—discrete mathematics, linear algebra, probability, statistics, and optimization—alongside programming (Python/C), data structures and algorithms, computer organization, operating systems, and databases, because robust ML demands both solid math and systems fluency. Foundational AI/ML courses introduce search and reasoning, supervised/unsupervised learning, model evaluation, bias-variance trade-offs, regularization, and feature engineering, before moving into deep learning (CNNs for vision, RNNs/transformers for sequences), NLP, and reinforcement learning; lab components emphasize implementing algorithms from scratch and then with modern frameworks like TensorFlow/PyTorch on realistic datasets. Data engineering and MLOps typically run in parallel: data modeling and management (SQL/NoSQL), data warehousing and ETL, Big Data systems, experiment tracking, model packaging, inference optimization, and CI/CD for ML so students can move beyond notebooks to reproducible pipelines and reliable services. Electives extend breadth into areas such as computer vision, information retrieval, attention/transformer architectures, edge AI, and cloud computing, while seminars and mini-projects cultivate literature review, reproducibility, and reporting skills valued in industry and research.

Later semesters emphasize systems integration and responsible AI. Courses and labs guide students to deploy models as APIs or embedded modules, benchmark latency/throughput on CPUs/GPUs, and apply quantization or distillation for edge targets, while tracking data lineage and monitoring drift in production-like setups. Responsible-AI modules cover explainability, robustness, privacy, and fairness, with structured exercises on auditing models and documenting risks. Credit structures often reveal balance and rigor; for example, one institution mandates about 160+ credits distributed across foundations, program core, program/open electives, independent learning, and industry interaction, ensuring depth in math/CS and repeated hands-on exposure via labs, projects, and internships. Many universities publish scheme-and-syllabus PDFs that detail unit-level topics—from regression/classification and SVMs to sequence models, attention mechanisms, and MLOps tasks—plus assessment patterns combining internal labs, end-semester exams, and semester-long projects. By capstone, teams are expected to define a well-scoped problem, build end-to-end pipelines (data acquisition to deployment), justify design choices with experiments, and present a reproducible artifact with documentation and model cards. Graduates thus exit with strong algorithmic understanding plus the engineering capabilities to take models live, aligning with current employer expectations across data science, ML engineering, and applied AI product roles.