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B.Tech. (MnC)

B. Tech in Mathematics and Computing - B.Tech. (MnC)

Program Overview Mathematics and Computing (MnC) is a fusion of Mathematics and Computer Science that has obtained wide acceptance as a distinct discipline over the past few years. It arises out of dealing with Mathematics as a fundamental tool in computing and with Computing as a primary component of mathematical problem solving. The program has been specially designed to meet the increasing needs of professionals who would be able to respond to the convergence between mathematical and computational problem solving. The program aims at expanding the mathematical, algorithmic and computational thinking of students and at providing sufficient and solid foundation for skill development in MnC. A strong mathematical foundation would enable the study and analysis of abstract concepts and to model many real life problems mathematically, algorithmic thinking would provide ways to solve these mathematical problems in an automated way and computational thinking would allow for evaluating the efficiency of these solutions.

The program aims to provide exposure to the students who wish to build a professional career in MnC, working at the cutting edge of technology, research and development. On successful completion of the program, the students would have acquired essential theoretical, technical and practical knowledge for solving real-world problems, and will have the ability to demonstrate excellent analytical, logical and problem solving skills. The students would have also acquired social and ethical attributes that would enable them in applying their skills for societal needs with effective communication – orally, in writing and on multi-media platforms.

Program Structure

The curriculum is organised with core courses, elective courses, internships and project works. The core courses are fundamental to building core competence and foundation for MnC domain knowledge areas. During senior years, students will have adequate choice of electives in order to dwell deeper into contemporary areas of their interest. A unique feature of the program is the mandatory independent project which is expected to give students a feel for their research ability in an area of their choice. The curriculum also includes a mandatory rural internship and a mandatory summer research/industrial internship. Finally, a student is required to take at least a semester long BTech Project (BTP) during which the students demonstrate their ability in using the knowledge and skills acquired during the program. The semester-wise structure of the curriculum is as follows:

The course structure of the curriculum is broadly classified into 3 categories.

  • Foundation Courses

    These are a set of compulsory/core courses required to be taken by every student enrolled in the program. Majority of foundation courses are offered in the first five semesters. These courses are from the technical areas of Computer Science and Information Technology (8 courses), Electronics and Communication (8 courses), as well as courses in Humanities and Social Sciences (4 courses), Mathematics and Basic Sciences (8 courses). A student earns 111 credits from these 28 Foundation courses.

  • MnC Electives Courses

    Courses of this category add to both the technical strength and humanities and social science skills of the students belonging to this program. The curriculum provides students a multi-track option, where a student can acquire knowledge in breadth as well as depth in multiple tracks through an appropriate choice of elective courses. A student is expected to earn 41 credits from these types of electives.

  • Internships and BTech project (BTP)

    A unique feature of the programs is the mandatory rural internship, which is expected to give the student a feel of his/her social milieu and is typically carried out with an NGO working in/for a rural setup. Its duration is of 3-4 weeks in the winter vacation falling after 3rd semester. After the completion of foundation courses, the student is required to take a 6-8 week Industrial/Research Internship. The student has a choice of taking an industrial internship or a research internship depending on his/her career goals. It is scheduled in the summer vacation falling after 6thsemester. Finally, the student is required to take a B.Tech. Project, during which he/she is required to demonstrate his/her ability to learn current areas of research and/or industrial interest, his/her ability to utilize the topics he/she has learnt during his/her stint in the program and his/her creative and design abilities. Usually this is done in the final semester, but it may be taken as a split BTP starting in 7th semester and concluding in the 8th semester. The current detailed course structure is given here under:

Semester-wise course sequence

Each course is associated with a fixed number of credits. Credits are awarded on an L-T-P-C system (C=L+T+P/2) per semester, that is, the number of contact hours for Lectures (L), Tutorials (T) and Practical (P) in a week. Nominally, since a semester has around 13–14 weeks of classes, therefore, a 3credit lecture course would amount to approximately 40 lecture hours in a semester.

Year I
YEAR – I
Semester I L-T-P-C Winter Break Semester II L-T-P-C
Mathematical, Algorithmic, and Computational Thinking 3-1-0-4   Functions of Single Variable and ODEs 3-1-0—4
Computer Organization and Programming
Computer Organization and Programming Lab
3-0-0—3
0-0-4—2
Object Oriented Programming 2-0-2—3
Discrete Mathematics 3-1-0—4 Data Structures and Algorithms 3-0-2—4
Digital Logic Design 1-0-2—2 Linear Algebra 3-1-0—4
Language and Literature 3-0-0—3 Approaches to Indian Society 3-0-0—3
Semester credits 18 Semester credits 18
         
Summer – I
         
Year II
             
  YEAR – II
  Semester III L-T-P-C Winter Break Semester IV L-T-P-C
  Probability and Random Processes 3-1-0—4 R U R A L   Internship Mathematical Statistics 3-1-0—4
  Operating Systems 3-0-2—4 Theory of Computation 3-1-0—4
  Design and Analysis of Algorithms 3-1-0—4 Parallel and Distributed, Algorithms 3-1-0—4
  Functions of Several variables and PDEs 3-1-0—4 Real and Complex Analysis 3-1-0—4
  Database Management Systems 3-0-2—4 Numerical and Computational Methods 3-0-2—4
  Science, Technology, Society 3-0-0—3 Environmental Studies 2-0-0—2
  Semester credits 23 0-0-8—4 Semester credits 22
   
  Summer – II
           
Year III
YEAR – III
Semester V L-T-P-C Winter Break Semester VI L-T-P-C
Mathematical Optimization 3-1-0—4   Machine Learning 3-0-2—4
Modelling and Simulation 3-0-2—4 Open Elective – 1 3-0-0—3
Algebraic Structures 3-1-0—4 MnC Elective – 3 3-0-0—3 /
3-1-0—4 /
3-0-2—4
Principles of Economics 3-0-0—3 MnC Elective – 4 3-0-2—4 /
3-1-0—4 /
3-0-0—3
MnC Elective – 1 3-0-2—4 /
3-1-0—4 /
3-0-0—3
MnC Elective – 5 3-0-2—4 /
3-1-0—4 /
3-0-0—3
MnC Elective – 2 3-0-2—4 /
3-1-0—4 /
3-0-0—3
Independent Project – 1/
MnC Elective – 6
0-0-6—3
3-0-2—4 /
3-1-0—4 /
3-0-0—3
Semester credits 21-23 Semester credits 19-23
 
Summer - III : Summer Research/Industrial Internship (0-0-12—6)
 
Year IV
YEAR – IV
Semester VII L-T-P-C Winter Break Semester VIII L-T-P-C
MnC Elective – 6 / Independent Project – 1 3-0-2—4 /
3-1-0—4 /
3-0-0—3
0-0-6—3
  MnC Elective – 10 / BTP-1 3-0-2—4 /
3-1-0—4 /
3-0-0—3
0-0-8—4
MnC Elective – 7 3-0-2—4 /
3-1-0—4 /
3-0-0—3 /
BTP-2 0-0-18—9
MnC Elective -8 3-0-2—4 /
3-1-0—4 /
3-0-0—3
 
Open Elective – 2 3-0-0—3
MnC Elective – 9 3-0-2—4 /
3-1-0—4 /
3-0-0—3
Independent Project – 2 / MnC Elective – 10 /
BTP – 1
0-0-6—3
3-0-2—4 /
3-1-0—4 /
3-0-0—3
0-0-8—4
Semester credits 18-23 Semester credits 12-13

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

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