B. Tech. in Mathematics and Computing

B. Tech. in Mathematics and Computing

. The department started a four year B.Tech. program in Mathematics and Computing from Academic year 2019-2020.


.  Admission to B.Tech. program in Mathematics and Computing is through JEE (Advanced) 2019-2020.



Background


Computer science is founded upon Mathematics. Further, computer science is also being used to understanding better and finding solutions to mathematical problems. Graduates of computer science with solid mathematical foundations are currently in great demand and will remain so due to heavy use of computers and computer assisted technologies in our day-to-day life. Keeping this in mind, Mathematics department in collaboration with computer science department is offering a 4-year B. Tech programme in Mathematics and Computing with an emphasis on Artificial Intelligence (AI). This program will give students a formidable combination of Mathematics and Computer Science by concentrating on areas where mathematics and computing are most relevant to each other. The program will prepare graduates well for advanced degrees and careers in a variety of fields in industry. The curriculum is designed in such a way that this programme provides a perfect platform for those who seek strong mathematical and analytical components with a specialization in AI. Artificial intelligence is bringing almost revolutionary changes in every field - healthcare, agriculture, security, banking & finance, e-commerce, education, sports, forensics, assistive technologies, etc.There is huge demand for AI talent. According to NITI Aayog report AI has the potential to add USD15.7 trillion to the global GDP by 2030 and roughly USD 1 trillion could be added to India GDP by 2035. The purpose of launching our new course - B.Tech in Mathematics and Computing is to develop a work force which can step up to the challenges of the upcoming age of artificial intelligence and computer assisted technologies where a strong mathematics foundation is required.


Salient Features of the Program

● The curriculum of this program is designed to meet the needs of mathematics in scientific investigations and recent technological innovations. The curriculum is designed in such a way that this programme provides a perfect platform for those who seek strong mathematical and analytical components with a specialization in artificial intelligence.
● The program core consists of 40% Mathematics, 30% core CS and 30% AI related courses.
● The students will be having flexibility to choose elective courses from a broad spectrum of courses depending on their interests.
● Majority of the courses are designed to give both the theoretical knowledge and practical training through Labs/Practicals/Case studies.
● Option to do projects in other departments/research labs/industries/foreign universities.
● Apart from capstone project, a six months industry internship option is available.


Career/Job Prospects:

  • The data from other IITs where similar programs are running, convey that this is a very successful program and provides a broad spectrum of very good career opportunities. Further, the program is one of the topmost choices with advanced JEE rankers.
  • We expect that the graduates from this program will get opportunities in world renowned companies related to information technology, banking & finance, machine learning assisted technologies.
  • The graduates could be Data Scientists in different domain.
  • We are expecting that program will help the students to bag MS/PhD positions in world renowned universities/Institutes in areas like Machine learning, Artificial Intelligence, Big Data, Mathematical Modeling, Economics and Finance, Image Processing, and many more.
  • The advent of Big Data and Data Analytics have created opportunities for Startup Companies with very small partners/employees strength. TBI IIT Ropar supports in house startups.


Semester-wise Course Structure:
Semester 1
Plan A (about 50% students)
Plan B (rest 50% students)
MA101 Calculus (3) [3-1-0-5-3]
MA101 Calculus (3) [3-1-0-5-3]
HS103 Professional English Communication (3) [2-2/3-2-13/3-3]
OR
HS102 English Language Skills (3) [2-2/3-2-13/3-3] instead, for students weak in English
HS103 Professional English Communication (3) [2-2/3-2-13/3-3]
OR
HS102 English Language Skills (3) [2-2/3-2-13/3-3] instead, for students weak in English
NC101 NCC I (1) [0-0-2-1-1] (NS101NSS I / NO101 NSO I only when NCC 1 not feasible for the student)
NC101 NCC I (1) [0-0-2-1-1] (NS101NSS I / NO101 NSO I only when NCC 1 not feasible for the student)
PH101 Physics for Engineers (5) [3-1-4-7-5]
CY101 Chemistry for Engineers (4) [3-1-2-6-4]
GE104 Introduction to Electrical Engineering (3)[2-2/3-2-13/3-3]
GE103 Introduction to Computer Programming & Data Structure (4.5) [3-0-3-15/2-4.5]
GE102 Workshop Practice (2) [0-0-4-2-2]
GE105 Engineering Drawing (1.5) [0-0-3-3/2-1.5]
HS101 History of Technology (1.5) [3/2-1/2-0-5/2-1.5]
HS101 History of Technology (1.5) [3/2-1/2-0-5/2-1.5]
Total Credits 18.5
Semester 2
Plan A (for those having Plan A in Sem-1)
Plan B (for those having Plan B in Sem 1)
MA102 Linear Algebra, Integral Transforms and Special Functions (3) [3-1-0-5-3]
MA102 Linear Algebra, Integral Transforms and Special Functions (3) [3-1-0-5-3]
Program Core (3 ) (viz. “Engineering Mechanics” [3-1-0-5-3] for ME and CE, “Introduction to Chemical Engineering” [3-1-0-5-3] for CH, “Discrete Mathematics” [3-1-0-5-3] for Math & Computing)
OR
Program-Specific General Engineering (3) (viz. “GE106 Materials Science for Electrical and Electronics Engineers” [3-1-0-5-3], for EE)
Program Core (3 ) (viz. “Engineering Mechanics” [3-1-0-5-3] for ME and CE, “Introduction to Chemical Engineering” [3-1-0-5-3] for CH, “Discrete Mathematics” [3-1-0-5-3] for Math & Computing)
OR
Program-Specific General Engineering (3) (viz. “GE106 Materials Science for Electrical and Electronics Engineers” [3-1-0-5-3], for EE)
NC102 NCC II (1) [0-0-2-1-1] OR
NO102 NSO II (1) [0-0-2-1-1] OR
NS102 NSS II (1) [0-0-2-1-1]
NC102 NCC II (1) [0-0-2-1-1] OR
NO102 NSO II (1) [0-0-2-1-1] OR
NS102 NSS II (1) [0-0-2-1-1]
CY101 Chemistry for Engineers (4) [3-1-2-6-4]
PH101 Physics for Engineers (5) [3-1-4-7-5]
GE103 Introduction to Computer Programming & Data Structure (4.5) [3-1-3-13/2-4.5]
GE104 Introduction to Electrical Engineering (3) [2-2/3-2-13/3-3]
GE105 Engineering Drawing (1.5) [0-0-3-3/2-1.5]
GE102 Workshop Practice (2) [0-0-4-2-2]
GE101 Technology Museum Lab (1) [0-0-2-1-1]
GE101 Technology Museum Lab (1) [0-0-2-1-1]
Total Credits 18


Semester- 3
Sr. Course Code Course Description L-T-P-S-C
1. CS201 Data Structures 3-1-2-6-4
2. MA411 Real Analysis 3-1-0-5-3
3. MA201 Differential Equations 3-1-0-5-3
4. EE201 Signals and Systems 3-1-0-5-3
5. NCIII/NOIII/NSIII NCC/NSO/NSS 0-0-2-1-1
6. HS201 / GE108 Economics / Basic Electronics 3-1-0-5-3 /
(2-2/3-2-13/3-3)
7. GE107 / GE109 Tinkering Lab / Introduction to Engineering Products [0 -0-3-3/2-1.5] /
[0 -0-2-1-1]
Total Credits 18 or 18.5
Semester- 4
Sr. Course Code Course Description L-T-P-S-C
1. MA204 Introduction to Numerical Analysis 3-1-0-5-3
2. MA426 Theory of computation 3-0-0-6-3
3. MA205 Computing Lab 0-0-4-2-2
4. MA202 Probability and Statistics 3-1-0-5-3
5. HS202 / BM101 Human Geography and Societal Needs /
Biology for Engineers
[1-1/3-4-11/3-3] /
[3-1-0-5- 3]
6. NCIV/NOIV/NSIV NCC/NSO/NSS 0 -0-2-1-1
7. HS201 / GE108 Economics/ Basic Electronics 3-1-0-5-3 /
(2-2/3-2-13/3-3)
8. GE107 / GE109 Tinkering Lab / Introduction to Engineering Products [0-0-3-3/2-1.5] /
[0-0-2-1-1]
Total Credits 19 or 19.5
Semester- 5
Sr. Course Code Course Description L-T-P-S-C
1. MA514 ANALYSIS & DESIGN OF ALGORITHMS 3-1-0-5-3
2. MA515 FOUNDATIONS OF DATA SCIENCE 3-0-2-7-4
3. MA301 COMPUTATIONAL ALGEBRA 3-0-0-6-3
4. HS202 / BM101 Human Geography and Societal Needs
/ Biology for Engineers
[1-1/3-4-11/3-3] /
[3-1-0-5- 3]
5. HS301 / GE111 Industrial Management
/ Introduction to Environmental Science & Engineering
3-1-0-5-3
6. HS104 Professional Ethics [about 50% students] 1-1/3-1-13/6-1.5
Total Credits 16 or 17.5
Semester- 6
Sr. Course Code Course Description L-T-P-S-C
1. To be added by the Department (Core Courses)      
2. CP301 Development Engineering Project 0-0-6-3-3
3. HS301 / GE111 Industrial Management
/ Introduction to Environmental Science & Engineering
3-1-0-5-3
4. HS104 Professional Ethics [about 50% students] 1-1/ 3-1-13/ 6-1.5
Total Credits     
 
Summer Vacation following Semester 6
Sr. Course Code Course Description L-T-P-S-C
1. II301 Industrial Internship and Comprehensive Viva Voce
(70% weightage for 8-week full internship and 30% for comprehensive viva on program fundamentals)
0-0-7-3.5-3.5
Total Credits 3.5
Semester- 7
Sr. Course Code Course Description L-T-P-S-C
1. CP302 Capstone Project I 0-0-6-3-3
ELECTIVE COURSES
2. HSXXX An English Language/Literature elective course in either 7th or 8th sem
for students who had “English Language Skills” in 1st Semester
3 Credits
3. BMXXX /MAXXX
/CYXXX /PHXXX
Science Maths Elective I 3 Credits
4. MAXXX Program Elective I 3 Credits
5. XXXXX Any extra credits taken under HS Elective
/Program Elective/Science Maths Elective
3 Credits
Total Credits 15 Credits
Semester- 8
Sr. Course Code Course Description L-T-P-S-C
1. CP303 Capstone Project II 0-0-6-3-3
ELECTIVE COURSES
2. HSXXX An English Language/Literature elective course in either 7th or 8th sem
for students who had “English Language Skills” in 1st Semester
3 Credits
3. BMXXX /MAXXX
/CYXXX /PHXXX
Science Maths Elective II 3 Credits
4. MAXXX Program Elective II 3 Credits
5. XXXXX Any extra credits taken under HS Elective
/Program Elective/Science Maths Elective
3 Credits
Total Credits 15 Credits
XXXXX denotes Open Elective Course
Grand Total: 145
Program Electives:
  1. Foundation of Data Science
  2. Data Mining
  3. Deep Learning
  4. Finance Mathematics
  5. Time Series Analysis
  6. Graph Theory
  7. Number Theory & Cryptography
  8. Functional Analysis
  9. Computational Partial Differential Equations
  10. Randomized Algorithms
  11. Game Theory
  12. Stochastic Process and Monte Carlo Simulation
  13. Applied Statistics
  14. Operating systems
  15. Data Base Management System
  16. Computer Architecture
  17. Approximation Algorithm
  18. Fuzzy Logic & Application
  19. Matrix Computation
  20. Applied Linear Algebra
  21. Complex Analysis
  22. Dynamical Systems
  23. Artificial Intelligence
  24. Algorithmic graph theory
  25. Combinatorial optimization
  26. Graphical Models
  27. Computer Vision
  28. Network Science
  29. Computational PDE
  30. Computational Fluid Dynamics
  31. Fluid Dynamics
  32. Advanced Data Structures