B. Tech in Mathematics and Computing - B.Tech. (MnC)
Mathematics and Computing is a natural meeting point of abstraction, structure, computation, and modelling. Many contemporary problems in science, engineering, finance, communication, data analysis, artificial intelligence, and decision-making require both mathematical maturity and computational competence. The B.Tech. program in Mathematics and Computing is designed to provide this integrated preparation. The curriculum combines core mathematics, theoretical computer science, algorithms, optimization, probability, stochastic processes, numerical methods, machine learning, software engineering, modelling, and computational algebra. Its purpose is to develop graduates who can move fluently between mathematical formulation, algorithmic design, computational implementation, and interpretation of results.
Nature of the Program
The program is a four-year undergraduate engineering degree in Mathematics and Computing. It is built around mathematical foundations, computing foundations, algorithmic and formal reasoning, computational modelling, and professional preparation. The program seeks to produce graduates who are comfortable with both theorem-based reasoning and implementation-based problem solving.
Program Vision
To develop graduates with strong mathematical foundations, algorithmic thinking, and computational competence, capable of formulating, analyzing, and solving complex problems in science, engineering, data-driven systems, and emerging areas of technology.
Program Mission
- M1. To provide rigorous training in mathematics, computing, algorithms, and modelling.
- M2. To develop the ability to translate real-world and abstract problems into mathematical and computational formulations.
- M3. To prepare students to design, analyze, implement, and evaluate algorithms, models, and software systems.
- M4. To cultivate analytical reasoning, proof skills, computational experimentation, and responsible professional practice.
- M5. To enable graduates to pursue higher studies, research, entrepreneurship, and professional careers in areas requiring mathematical and computational depth.
Program Outcomes (POs)
| PO No. | Program Outcomes |
|---|---|
| PO1 | Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems. |
| PO2 | Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences |
| PO3 | Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations. |
| PO4 | Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions. |
| PO5 | Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations. |
| PO6 | The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice. |
| PO7 | Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development. |
| PO8 | Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice. |
| PO9 | Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings. |
| PO10 | Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions. |
| PO11 | Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments. |
| PO12 | Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change. |
The Program Specific Outcomes (PSOs) set the following goal:
| PSO No. | Program Specific Outcomes (PSOs) |
|---|---|
| PSO1 | Mathematical Modelling and Analysis: Formulate, analyze, and solve mathematical models arising from deterministic, stochastic, discrete, continuous, and data-driven systems. |
| PSO2 | Algorithmic and Computational Problem Solving: Design, analyze, implement, and evaluate algorithms and computational methods using appropriate data structures, mathematical foundations, and complexity considerations. |
| PSO3 | Scientific and Data-Driven Computing: Use numerical, statistical, simulation, optimization, and machine learning techniques to solve computational problems and interpret results critically. |
| PSO4 | Formal Reasoning and Foundations: Apply proof techniques, discrete mathematics, logic, automata theory, computability, algebraic structures, and number-theoretic methods to reason about computation and mathematical systems. |
Program Outcomes (POs) & Course Outcomes (COs) of The Program
Curriculum Philosophy
The curriculum is organized to support progressive development from foundational knowledge to advanced computational and mathematical competence. The curriculum progresses through the following stages:
Foundation Stage:
In the first year, students build foundations in programming, calculus, linear algebra, electronics, digital logic, data structures, and mathematical foundations of computer science. This stage develops basic mathematical language, computational thinking, programming ability, and reasoning.
Core Mathematics and Algorithms Stage:
In the second year, the curriculum moves toward core disciplinary depth. Courses in real and complex analysis, optimization, design and analysis of algorithms, differential equations, stochastic processes, machine learning, and software engineering develop mathematical maturity, algorithmic competence, modelling ability, and software development practice.
Computational Methods Stage:
In the third year, students study numerical and transform techniques, modelling and simulation, theory of computation, and computational algebra and number theory. These courses consolidate the link between mathematical structure and computational method. Electives allow students to pursue specialized interests.
Specialization Stage:
In the final year, electives, internships, and project work provide opportunities for independent study, practical exposure, and integration of knowledge. Students may develop depth in theoretical computer science, algorithms, artificial intelligence, data science, scientific computing, computational mathematics, or related areas. The curriculum thus supports both breadth and depth. It develops graduates who can understand formal structures, design computational methods, implement them, and evaluate their results.
Course Categories
- Institute Core (IC): Mandatory courses common to all B.Tech. students under the Common Curriculum Framework.
- Program Core (PC): Mandatory courses specific to the CSAI program.
- Program Electives (PE): Elective courses in the primary areas of the CSAI program.
- Free Electives (FE): Courses outside the primary area of the program; at least one Free Elective should be from HSSE.
Semester-wise course sequence
Semester 1
| Sr. No. | Course Code | Course Name | Credits |
|---|---|---|---|
| 1 | IC-100 | HSS-I: Language and Literature | 3-0-0-3 |
| 2 | IC-102 | Introduction to Programming | 3-0-0-3 |
| 3 | IC-103 | Programming Lab | 0-1-2-2 |
| 4 | IC-104 | Basic Electronics | 3-0-2-4 |
| 5 | IC-105 | Maths-I: Calculus | 3-1-0-4 |
| 6 | MC-101 | Mathematical, Algorithmic, and Computational Thinking | 3-1-0-4 |
| 7 | IC-106 | Co-curricular Activities - 1 | 0-0-2-1 |
Semester 2
| Sr. No. | Course Code | Course Name | Credits |
|---|---|---|---|
| 1 | IC-107 | HSS-II: Approaches to Indian Society | 3-0-0-3 |
| 2 | IC-108 | Data Structures | 3-0-2-4 |
| 3 | IC-109 / IC-110 | Digital Logic / Computer Organization | 3-0-2-4 |
| 4 | IC-111 | Maths-II: Linear Algebra | 3-1-0-4 |
| 5 | IC-112 | Language in Practice | 0-1-2-2 |
| 6 | MC -102 | Mathematical Foundations of Computer Science | 3-1-0-4 |
| 7 | IC-113 | Design Thinking for Engineers | 0-x-x-2 |
| 8 | IC-114 | Co-curricular Activities - 2 | 0-0-2-1 |
Semester 3
| Sr. No. | Course Code | Course Name | Credits |
|---|---|---|---|
| 1 | IC-215 | HSS-III: Science, Technology, and Society | 3-0-0-3 |
| 2 | IC-216 | Object Oriented Programming | 1-0-2-2 |
| 3 | IC-217 | Maths-III: Probability and Statistics | 3-1-0-4 |
| 4 | MC-203 | Real and Complex Analysis | 3-1-0-4 |
| 5 | MC-204 | Optimization Techniques | 3-0-2-4 |
| 6 | MC-205 | Design and Analysis of Algorithms | 3-1-0-4 |
| 7 | IC-218 | Co-curricular Activities - 3 | 0-0-2-1 |
| 8 | IC-219 | Exploration Project | 0-0-2-1 |
Rural Internship: 2 credits (The structure may change as decided by the RI committee)
Semester 4
| Sr. No. | Course Code | Course Name | Credits |
|---|---|---|---|
| 1 | IC-220 | Environmental Studies | 3-0-0-3 |
| 2 | IC-221 | Introduction to Machine Learning | 3-0-2-4 |
| 3 | MC-206 | Differential Equations | 3-1-0-4 |
| 4 | MC-207 | Applied Stochastic Processes | 3-1-0-4 |
| 5 | MC - 208 | Software Engineering | 3-0-2-4 |
| 6 | MnC PE-201 | Program Elective - 1 | 3-x-x-3/4 |
| 7 | IC-222 | Co-curricular Activities - 4 | 0-0-2-1 |
Semester 5
| Sr. No. | Course Code | Course Name | Credits |
|---|---|---|---|
| 1 | IC-323 | Principles of Economics | 3-0-0-3 |
| 2 | MC 309 | Numerical and Transform Techniques | 3-0-2-4 |
| 3 | MC 310 | Modelling and Simulation | 3-0-2-4 |
| 4 | MC 311 | Theory of Computation | 3-1-0-4 |
| 5 | MnC PE-302 | Program Elective - 2 | 3-x-x-3/4 |
Semester 6
| Sr. No. | Course Code | Course Name | Credits |
|---|---|---|---|
| 1 | MC -312 | Computational Algebra and Number Theory | 3-0-2-4 |
| 2 | MnC PE-303 | Program Elective - 3 | 3-x-x-3/4 |
| 3 | MnC PE-304 | Program Elective - 4 | 3-x-x-4 |
| 4 | FE-301 | Free or HSS Elective | 3-x-x-3/4 |
| 5 | MnC PE-305 | Program Elective - 5 | 3-x-x-3/4 |
Summer Internship: 4 credits
Notes:
- Instead of taking MnC PE-305 in Semester VI, a student may take this course in Semester VII as MnC PE-405. This creates flexibility allowing a course load of 4 courses in Semester VI.
- FE-301 is a non-MnC PE course. If this is not from the discipline of HSS, then FE-402 should be from the HSS discipline.
Semester 7
| Sr. No. | Course Code | Course Name | Credits |
|---|---|---|---|
| 1 | MnC PE-405 / MnC PE-409 | Program Elective - 5 / 9 | 3-x-x-3/4 |
| 2 | MnC PE-406 | Program Elective - 6 | 3-x-x-4 |
| 3 | MnC PE-407 | Program Elective - 7 | 3-x-x-4 |
| 4 | MnC PE-408 | Program Elective - 8 | 3-x-x-3/4 |
| 5 | FE-402 | Free or HSS Elective | 3-x-x-3/4 |
Notes:
- Instead of MnC PE-405 in Semester VII, a student can take this course in Semester VI as MnC PE-305, allowing a reduced load of 4 courses in Semester VII.
- If MnC PE-305 is completed in Semester VI, the student may take MnC PE-409 in Semester VII, allowing up to nine program electives without exceeding five courses in each of Semesters VI and VII.
- FE-402 is a non-MnC PE course. If FE-301 is not from the discipline of HSS, then FE-402 must be from the HSS discipline.
Semester 8
| Sr. No. | Course Code | Course Name | Credits |
|---|---|---|---|
| 1 | IC-424 | BTP / ITP | 12 |
Note:
- The codes for the electives both Program Electives and Free Electives are all place holders and actual codes will vary depending on the courses opted for by students.
Representative list of electives
- Graph Theory and Algorithms
- Approximation Algorithms
- Computational Complexity
- Randomized Algorithms
- Quantum Computing
- Introduction to Cryptography
- Blockchain and Cryptocurrencies
- Adversarial Machine Learning
- Machine Learning and Security
- Introduction to coding theory and Applications
- Data Mining and Visualization
- Human Computer Interaction
- Natural Language Processing
- Network Science
- Time Series Analysis
- Software Engineering
- Hypothesis Testing
- Multivariate Statistics
- Bayesian Analysis
- Financial Data Analysis
- Machine Learning in Finance
- Stochastic Simulation
- Dynamical Systems
- Computational Number Theory
- Fluid Dynamics
- Game Theory
- Queuing theory
- Operations Research
- Functional Analysis
- Stochastic calculus for finance
- Computational Finance
Admission Process
Details on the application process, admission criteria, fee structure and financial assistance can be found here