4-Year Curriculum & Unit Credit Matrix
Comprehensive Semester Syllabi Breakdown & University Credit Distribution
The B.Tech program in Artificial Intelligence and Data Science is meticulously structured over eight semesters, comprising a total of 160 credits. This curriculum matrix is designed to progressively build a robust foundation in mathematical logic, programming architectures, and specialized AI subfields. The following sections delineate the semester-wise distribution of core subjects, laboratory workloads, and their corresponding academic weightings.
Tier 1: First Year (FY) - Semester 1 & Semester 2
The first year focuses on establishing the essential bedrock of engineering mathematics and basic computational thinking. Students transition from high school paradigms into rigorous academic engineering standards.
Semester 2 Credit Allocation
- Fundamentals of Programming (FPP): 4 credits
- Engineering Physics: 4 credits
- Engineering Mathematics II: 4 credits
- Introduction to Artificial Intelligence (AI): 3 credits
- Fundamentals of Computer Systems & Networks (FCS&N): 3 credits
Tier 2: Second Year (SY) - Semester 3 & Semester 4
The second year marks a significant inflection point as the curriculum dives deep into core computer science principles and data structures, serving as the prerequisites for advanced machine learning models.
// Semester 3 Subjects & Credits
Advanced Data Structures (ADS) : 4 credits
Database Management Systems (DBMS) : 4 credits
Discrete Mathematical Structures(DMS): 3 credits
Digital Electronics : 3 credits
// Semester 4 Subjects & Credits
Operating Systems (OS) : 4 credits
Design and Analysis of Alg. (DAA) : 4 credits
Theory of Computation (TOC) : 3 credits
Probability & Statistics : 3 credits
Tier 3: Third Year (TY) - Semester 5 & Semester 6
In the third year, the program intensifies its focus on specialized domain knowledge. Modules heavily emphasize Machine Learning algorithms, Deep Learning neural networks, Big Data analytics frameworks, and Cloud Computing architectures. Students engage in complex mathematical modeling, exploring optimization techniques, loss functions, and backpropagation, while simultaneously deploying scalable solutions on modern cloud infrastructure.
Tier 4: Final Year - Semester 7 & Semester 8
The final year represents the culmination of the academic journey, heavily weighted towards application and research. It features extensive capstone projects alongside advanced elective modules in Natural Language Processing (NLP), Computer Vision, and MLOps. Students are expected to demonstrate end-to-end engineering capabilities, from dataset curation to model deployment and life-cycle management, preparing them for immediate industry integration or graduate research.
Evaluation Methodology
The academic evaluation is strictly bifurcated to assess both continuous learning and summative knowledge retention. The Continuous Internal Evaluation (CIE) carries a 40% weightage, encompassing mid-term examinations, quizzes, and assignments. The End Term Examination (ETE) dictates the remaining 60% weightage, enforcing rigorous academic standards. Furthermore, practical lab assessments and project evaluations involve continuous viva-voce and implementation reviews to ensure practical competency aligns with theoretical knowledge.
"A well-structured curriculum is not merely a list of subjects, but a logical sequence of cognitive challenges designed to forge an adaptable engineering mindset."