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About the Department

 

Vision

      Impart outcome based education, focusing on quality teaching with an emphasis on Mathematical modeling and sustainable interdisciplinary research.

Mission

  •       To create infrastructure and resources for quality teaching
  •       To train graduates with mathematical background for solving complex engineering problems
  •       Enable to acquire latest skills of experiential learning for problem analysis and develop solutions
  •       Create an ambience for sustainable interdisciplinary research

Long Term Goals

  • To make every faculty attain PhD through research centre facility.

  • To establish centre of mathematical modeling and incubation centers.

  • To introduce new courses as elective subjects in tune with state-of-art technology.

  • To organize International / National conferences periodically.

  • To focus more on research with industrial projects handling.

Short Term Goals

  • To introduce more electives in UG Programs.

  • To achieve 100% pass in the next 5 years.

  • To have more Ph.D. qualified faculty.

  • To take-up R&D and Consultancy activities.

  • To have at least 20 research paper publications by the faculty members yearly.

  • To take up DST / AICTE sponsored projects, jointly with engineering departments.

Program Educational Objectives (PEOs):

  • The Department of Mathematics educates undergraduate (BE) and postgraduate (M.TECH) students with a course characterized by art of teaching with- mathematical modeling skills, assignments, student project work / seminars, proper counseling and an active involvement of students in their learning.

  • The Engineering Mathematics has wide applications and skills applicable to multidisciplinary technology in a balanced, integrated engineering curriculum.

  • We provide an education that is strong in fundamentals and applied knowledge which enables them for graduate study and immediate selection in a variety of fields as well as for lifelong learning.

Course Outcomes:

  • Analyze and develop mathematical models to solve simple physical problems.

  • Understand the significance of fundamental concepts of Mathematics in various Engineering problems.

  • Apply effectively appropriate quantitative tools and logical modes of thinking to analyze for solving Engineering problems.

  • Identify random phenomena to analyze and interpret probabilistic models.

  • Evaluate, formulate and solve Engineering problems that require the techniques of Numerical analysis and partial differential equations.

  • Ability to combine the various mathematical concepts, predict suitable model & solution methods.

  • Justify the overall mathematical knowledge gained to interpret and provide a solid foundation for future learning.

Academic Program

Courses Offered: UG

Sl. No

Name of the Programme

Branches

Course Title (with code)

1

I Semester B.E.

Common to all

Multivariable Calculus(21MA11)
Common to all Engineering Mathematics-I (18MA11)
2

II Semester B.E.

Common to all

Differential Equations and Numerical Methods (21MA21)
Common to all Engineering Mathematics-II (18MA21)
3 III Semester B.E. CSE, ISE Linear Algebra, Laplace Transforms and Number Theory (18MA31A)
ECE, EEE, EIE, TCE Discrete and Integral Transforms (18MA31B)
ASE, BT, CE, CH, IEM, ME Engineering Mathematics-III (18MA31C)
4 IV Semester B.E. CSE, ISE Graph theory, Statistics and Probability theory (18MA41A)
ECE, EIE, TCE Linear Algebra, Statistics and Probability theory (18MA41B)
ASE, CE, CH, ME Engineering Mathematics-IV (18MA41C)
5
 III Semester B.E. -Bridge Course.

For lateral Entry students

(ASE, BT, CE, CH, IEM, ME, ECE, EEE, EI, ET)

 

 Bridge Course Mathematics (18DMA37)
6

IV Semester B.E.

-Bridge Course.

 For lateral Entry students

 (CSE, ISE)

 Bridge Course Mathematics (18DMA48)
7 V Semester B.E. Common to all COMPUTATIONAL ADVANCED NUMERICAL METHODS (18G5B16)(Global Elective)
MATHEMATICS FOR MACHINE LEARNING (18G5B17)(Global Elective)
8 VI Semester B.E. Common to all

Advanced Statistical Methods (18G6E15)
Mathematical Modelling (18G6E16)

 

 

Global Elective Syllabus:

  1. Advanced Statistical Methods (18G6E15)
  2. Mathematical Modelling (18G6E16)
  3. COMPUTATIONAL ADVANCED NUMERICAL METHODS (18G5B16)
  4. MATHEMATICS FOR MACHINE LEARNING (18G5B17)

Course offered: PG

Sl.No

Name of the Programme

Branch

Semester

Course Title (with code)

1

M.Tech. Biotechnology

BT

 I

Applied Mathematics (18MAT11A)
M.Tech. Bioinformatics

2

M.Tech. Highways

CV

 I

Applied Mathematics (18MAT11A)
M.Tech. Structures

3

M.Tech. Machine Design

ME

 I

Applied Mathematics (18MAT11A)
M.Tech. Computer Integrated Manufacturing
M.Tech. Product Design & Manufacturing

4

M.Tech. Power Electronics

EEE

 I

Applied Mathematics (18MAT11A)

5

M.Tech. Chemical Engg

 CHEM I

Applied Mathematics (18MAT11A)            
6 M.Tech. Computer Science Engg. CSE I Probability Theory and Linear Algebra (18MAT11B)
M.Tech. Computer Network Engg.
7 M.Tech. Software Engg. ISE I Probability Theory and Linear Algebra (18MAT11B)
M.Tech. Information Technology
8 M.Tech. Digital Communication System TCE I Probability Theory and Linear Algebra (18MAT11B)
M.Tech. Radio Frequency and Microwave Engg.
9 M.Tech. Communication Systems ECE I Probability Theory and Linear Algebra (18MAT11B)
10 M C A MCA I Discrete Mathematics (18MAT11)
11 M. Tech. Common to all II Advanced Statistical Methods (18MAT2G10) (Global Elective)

 

 

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