Curriculum Overview
The Mathematics program at Ahmedabad University Ahmedabad is designed to provide students with a rigorous foundation in both pure and applied mathematics, preparing them for careers in research, industry, and academia. The curriculum is structured across eight semesters, integrating core mathematical concepts with practical applications through laboratory work, project-based learning, and industrial collaborations.
Course Structure
The program offers a blend of core courses, departmental electives, science electives, and laboratory sessions that collectively build a comprehensive understanding of mathematical principles. Each course is carefully crafted to ensure theoretical depth while promoting practical implementation through hands-on experience.
| Semester | Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
| 1 | MATH101 | Calculus I | 3-1-0-4 | - |
| 1 | MATH102 | Linear Algebra | 3-1-0-4 | - |
| 1 | MATH103 | Introduction to Programming | 2-0-2-3 | - |
| 1 | PHYS101 | Physics for Engineers | 3-1-0-4 | - |
| 1 | CHEM101 | Chemistry | 3-1-0-4 | - |
| 1 | HSS101 | English for Academic Writing | 2-0-0-2 | - |
| 2 | MATH201 | Calculus II | 3-1-0-4 | MATH101 |
| 2 | MATH202 | Differential Equations | 3-1-0-4 | MATH101 |
| 2 | MATH203 | Probability and Statistics | 3-1-0-4 | MATH101 |
| 2 | MATH204 | Discrete Mathematics | 3-1-0-4 | - |
| 2 | CS201 | Object-Oriented Programming | 2-0-2-3 | MATH103 |
| 2 | HSS201 | Cultural Studies | 2-0-0-2 | - |
| 3 | MATH301 | Real Analysis | 3-1-0-4 | MATH201 |
| 3 | MATH302 | Abstract Algebra | 3-1-0-4 | MATH201 |
| 3 | MATH303 | Complex Analysis | 3-1-0-4 | MATH201 |
| 3 | MATH304 | Numerical Methods | 3-1-0-4 | MATH201 |
| 3 | MATH305 | Mathematical Modeling | 3-1-0-4 | MATH203 |
| 3 | CS301 | Data Structures and Algorithms | 2-0-2-3 | CS201 |
| 4 | MATH401 | Topology | 3-1-0-4 | MATH301 |
| 4 | MATH402 | Optimization Theory | 3-1-0-4 | MATH301 |
| 4 | MATH403 | Advanced Probability | 3-1-0-4 | MATH203 |
| 4 | MATH404 | Mathematical Biology | 3-1-0-4 | MATH305 |
| 4 | MATH405 | Scientific Computing | 3-1-0-4 | MATH304 |
| 4 | CS401 | Database Systems | 2-0-2-3 | CS301 |
| 5 | MATH501 | Advanced Numerical Analysis | 3-1-0-4 | MATH304 |
| 5 | MATH502 | Stochastic Processes | 3-1-0-4 | MATH403 |
| 5 | MATH503 | Financial Mathematics | 3-1-0-4 | MATH203 |
| 5 | MATH504 | Machine Learning | 3-1-0-4 | MATH305 |
| 5 | MATH505 | Cryptography | 3-1-0-4 | MATH302 |
| 5 | ECE501 | Signal Processing | 3-1-0-4 | - |
| 6 | MATH601 | Mathematical Optimization | 3-1-0-4 | MATH402 |
| 6 | MATH602 | Research Project I | 0-0-6-6 | - |
| 6 | MATH603 | Advanced Topics in Applied Mathematics | 3-1-0-4 | MATH305 |
| 6 | MATH604 | Advanced Mathematical Modeling | 3-1-0-4 | MATH305 |
| 6 | MATH605 | Computational Fluid Dynamics | 3-1-0-4 | MATH304 |
| 7 | MATH701 | Research Project II | 0-0-6-6 | - |
| 7 | MATH702 | Thesis Proposal | 0-0-3-3 | - |
| 8 | MATH801 | Final Year Thesis | 0-0-12-12 | - |
| 8 | MATH802 | Internship | 0-0-0-6 | - |
Advanced Departmental Electives
A wide array of advanced departmental electives is offered to cater to diverse interests and career paths. These courses are designed to deepen understanding in specialized areas and provide practical skills essential for professional growth.
- Mathematical Biology: This course explores the application of mathematical techniques to biological systems. Students learn about population dynamics, epidemiological models, and biochemical networks using differential equations and stochastic processes. The course includes laboratory sessions involving data analysis and simulation software such as MATLAB and R.
- Advanced Probability: The course delves into advanced concepts such as martingales, Brownian motion, and limit theorems in probability theory. It prepares students for research in stochastic analysis and financial modeling, with practical applications in risk assessment and derivative pricing.
- Financial Mathematics: Focused on pricing derivatives, portfolio optimization, and risk management, this course equips students with tools to analyze financial markets using mathematical models. The curriculum includes topics such as Black-Scholes model, Monte Carlo simulations, and fixed income securities.
- Cryptography: Students study modern cryptographic protocols, including public key cryptography, hash functions, and digital signatures. Practical sessions involve implementing encryption algorithms in software environments, with a focus on real-world applications in cybersecurity.
- Machine Learning: Covering supervised and unsupervised learning, neural networks, and deep learning architectures, this course prepares students for AI-driven applications in industry. The curriculum includes hands-on projects using Python libraries like scikit-learn and TensorFlow.
- Optimization Theory: This course introduces linear programming, integer programming, and convex optimization methods used in engineering, economics, and computer science. Students engage in case studies and real-world problem-solving exercises to apply theoretical concepts.
- Stochastic Processes: Students explore Markov chains, Poisson processes, and queueing theory, applying these concepts to real-world problems in telecommunications, finance, and operations research. The course emphasizes both theoretical foundations and practical applications.
- Scientific Computing: Using programming languages like Python and MATLAB, students learn numerical methods for solving scientific problems, including finite difference schemes and iterative solvers. The course includes laboratory sessions on modeling physical phenomena and analyzing large datasets.
- Topology: The course introduces topological spaces, continuity, compactness, and connectedness, providing a foundation for advanced mathematical analysis and applications in data science. Students engage in abstract reasoning and problem-solving exercises to understand topological properties.
- Advanced Numerical Analysis: This course focuses on numerical integration, interpolation, and solving partial differential equations using finite element methods. The curriculum includes programming assignments and computational projects to reinforce theoretical concepts.
Project-Based Learning Framework
The department places a strong emphasis on project-based learning, integrating theoretical knowledge with practical implementation throughout the program. Mini-projects are introduced in the second year and are embedded within core courses to reinforce learning outcomes.
Mini-projects span various domains such as data analysis, algorithm design, modeling of physical phenomena, and mathematical software development. Students work in teams under faculty supervision, presenting their findings at departmental symposiums and competitions.
The final-year capstone project or thesis is a significant component of the program, requiring students to undertake original research or substantial application-oriented work. Projects are selected based on student interests and career goals, often involving collaboration with industry partners or research institutions.
Faculty mentors guide students through the research process, from problem formulation to data collection, analysis, and presentation. The thesis component requires students to demonstrate mastery in their chosen area, culminating in a comprehensive report and oral defense before a panel of experts.