Core courses

Mathematics: Analysis and Linear Algebra I, II, Probability and Statistics, Basic Analysis, Introduction to Algebraic Structures.

Computing: Algorithms and Programming, Introduction to Computer Systems, Discrete Mathematics, Data Structures and Algorithms, Automata Theory and Computability, Introduction to Numerical Methods.

EECS: Introduction to Electronics and Electrical Engineering, Introduction to AI and ML.

Breadth Soft Core: 12 credits from a selection of Physics, Chemistry, Biology, Material Science, Earth and Environmental Science subjects.

Humanities: 9 credits.

Soft Core Courses

The soft core consists of the Mathematics and Computing streams. Students have to take at least 6 credits in each stream from the specified lists of courses. Students have to take at least 21 credits from the list of soft core courses. Courses from the list of soft core courses can also be taken as electives.

For details on these Computing Courses, please click here.

  • E0 205 Mathematical Logic and Theorem Proving
  • E0 206 Theorist’s Toolkit
  • E0 207 Program Analysis and Verification
  • E0 209 Principles of Distributed Software
  • E0 220 Graph theory
  • E0 224 Computational Complexity Theory
  • E0 225 Design and Analysis of Algorithms
  • E0 228 Combinatorics
  • E0 230 Computational Methods of Optimization
  • E0 235 Cryptography
  • E0 244 Computational Geometry and Topology
  • E0 248 Theoretical Foundations of Cryptography
  • E0 259 Data Analytics
  • E0 265 Convex Optimization and Applications
  • E0 267 Soft Computing
  • E0 270 Machine Learning
  • E0 272 Formal Methods in Software Engineering
  • E1 213 Pattern Recognition and Neural Networks
  • E1 222 Stochastic Models and Applications (3:0)
  • E1 244 Detection and Estimation
  • E1 251 Linear and Nonlinear Optimization
  • E1 254 Game Theory
  • E1 277 Reinforcement Learning
  • E2 201 Information Theory
  • E2 202 Random Processes
  • E2 232 TCP/IP Networking
  • E2 230 Network Science and Modelling
  • DS 211 Numerical Optimization
  • DS 221 Introduction to Scalable System
  • DS 250 Multigrid Methods
  • DS 256 Scalable Systems for Data Science
  • DS 284 Numerical Linear Algebra
  • DS 289 Numerical Solution to Differential Equations
  • DS 291 Finite Elements: Theory and Algorithms,
  • DS 294 Data Analysis and Visualization
  • DS 295 Parallel Programming
  • DS 301 Bioinformatics
  • HE 384 Quantum Computation
  • UMC 204 Digital Systems Design (3:0)
  • BE 218 Computational Epidemiology (3:1)

Semester-wise Course Requirements

For details on these courses, please click here.

UMA 101 Analysis and Linear Algebra I4:0
UENG 101 Algorithms and Programming3:1
UH 101 Ways of Knowing2:0
Any two of: UBL101 Biology I UCY101 Physical Chemistry UPH101 Physics I3:1+3:1
UMA 102 Analysis and Linear Algebra II4:0
UENG102 Electrical and Electronics Engg3:1
UMC 102 Computer Systems3:0
Any one of: UBL102 Biology II, UCY102 Inorganic Chemistry, UPH102 Physics II (Elec-Mag-Optics), UENG 103 Earth & Env3:1/3:0
UMC 103 Discrete Mathematics2:0
UH 102 Ways of Seeing2:0
Reduced Load: drop one course other than UMA 102, UMC 102, UMC 10314-17
UMA 201 Probability and Statistics4:0
UMC 201 Data Structures & Algo3:1
UMC 202 Numerical Methods3:1
Any one of: UPH201 Physics III, UBL201 Biology III, UCY201 Chemistry III, UENG201 Materials 3:1/3:0
UH 201 Ways of Doing2:0
Reduced Load: drop one course other than UMA 201, UMC 201, UMC 202  14-16
UM 204 Analysis3:1
UM 205 Algebraic Structures3:1
UMC 203 AI and ML3:1
UMC 205 Automata and Computability3:1
UH 203 Folk Arts1:0
Reduced Load: drop one course13-16
Enhanced Load17-21
UH 301 Journalism for Scientists1:0
Softcore/ Electives16
Normal  17-19
Enhanced Load17-21
UH 302 Governance1:0
Softcore/ Electives16
Enhanced Load17-21
UMC 401 ISP I / Softcore/ Electives6
Normal  12-16
Enhanced Load12-21
UMC 402 ISP II / Electives (6) + Electives (6) OR UMC 403 Project (12)12  
Enhanced Load12-21 
  • Students who have not opted for ISP-I are eligible for the 12 credit research project provided they have a CGPA of 7.0 or above at the end of the seventh semester.
  • Students who have opted for ISP-I are eligible for ISP-II provided they do not have a C grade or lower in ISP-I.
  • Students who opted for ISP-I are eligible for the 12 credit research project provided they do not have a B grade or lower in ISP-I.

All students must complete a total of at least 128 credits comprising courses and other components like projects, as specified in the course requirements above. The course load for the first semester is fixed. Each subsequent semester has a “Normal”, “Reduced” and “Enhanced” course load, as specified in Table 1. Based on their CGPA and previous-term TGPA, students must register for an appropriate course load as specified in Table 2 below. In Semesters II and III, only courses listed in Sems I to IV can be credited towards an Enhanced load. Any deviation from the recommended load will be allowed only with the permission of the Dean.

Recommended Course Load

CriteriaCourse Load
CGPA ≤ 6.0 AND Prev-TGPA ≤ 5.5Reduced in Sems II to IV, Normal in Sems V to VIII
6.0 < CGPA < 8.0 OR 5.5 < Prev-TGPA < 8.0Normal in Sems II to VIII
CGPA ≥ 8.0 AND Prev-TGPA ≥ 8.0Normal in Sems II and III, Enhanced in Sems IV to VIII

Continuing for MTech Degree:

Students have the option to continue for their 5th year to obtain an MTech degree in Mathematics and Computing. To be eligible to exercise this option, students must:
(a) have completed all the requirements of the BTech Mathematics and Computing degree at the end of their 8th semester; and
(b) have a CGPA of 7.0 or more at the end of their 8th semester.

To obtain an MTech degree, students need to complete 32 credits with the following breakup:
* 13 credits of courses in the 9th and 10th semesters.   Students are required to have completed a minimum of 10 credits of   courses from the Mathematics Soft Core Pool and 10 credits of courses from   the Computing Soft Core Pool, across the 10 semesters of the BTech/MTech   programme.
* A project of 19 credits in the 9th and 10th semesters.

Study Tracks

The programme structure encourages interested students to pursue a study track should they wish to do so. Here is an indicative list of study tracks and their corresponding courses.

  • Linear Algebra, Multivariable calculus.
  • Algebra: Algebra-I, Algebra-II, Number theory, Graph theory, Cryptography.
  • Analysis: Measure and Integration, Functional Analysis, ODE, PDE, Convex Optimization, Numerical solutions to Differential Equations.
  • Geometry/Topology: Topology, Differentiable Manifolds, Algebraic Topology, Graph theory, Computational Geometry and Topology.
  • Probability: Measure and Integration, Probability Theory, Random Processes, Stochastic Processes, Percolation and Random Graphs, Random Matrix Theory.
  • Random Processes
  • Computational Methods of Optimization
  • Machine Learning, Game Theory
  • Data Mining
  • Soft Computing
  • Reinforcement Learning
  • Scalable Systems for Data Science
  • Foundations of Robotics
  • Data Analysis and Visualization
  • Numerical Optimization
  • Multigrid Methods
  • Numerical Solution of Differential Equations
  • Finite Element Methods, Bioinformatics.
  • Algorithms and Complexity: Design and Analysis of Algorithms, Theorist’s Toolkit, Computational Complexity Theory, Introduction to Randomized Algorithms, Approximation Algorithms. 
  • Combinatorics and Geometry: Computational Geometry, Computational Topology, Combinatorics, Graph Theory. 
  • Cryptography and Security: Theoretical Foundations of Cryptography, Cryptography, Network and Distributed Systems Security, Foundations of Secure Computation.
  • Logic and Verification: Mathematical Logic and Theorem Proving, Formal Methods in Software Engineering, Program Analysis and Verification.
  • Mechanics
  • Electricity, Magnetism and Optics
  • Quantum Mechanics I
  • Physics/Engineering foundations of Quantum Technologies
  • Introduction to Quantum Computation
  • Introduction to Quantum Communication & Cryptography
  • Advanced Quantum Computing & Information.
  • Biology for Engineers
  • Fundamentals of Bio Engineering I,II
  • Theoretical and Mathematical Ecology
  • Dynamical Systems in Biology
  • Introduction to Molecular Simulation
  • Current Trends in Drug Discovery
  • Digital Epidemiology 
  • Neural Signal Processing
  • Theoretical and Computational Neuroscience
  • Algorithmic foundations of Big Data Biology
  • Detection and estimation theory
  • Random processes 
  • Linear and non-linear optimization
  • Matrix theory or Computational linear algebra
  • Pattern recognition and neural networks
  • Signal processing in practice 
  • Digital Signal Processing
  • Digital Image Processing
  • Neural Signal Processing
  • Probability Theory
  • Stochastic Finance
  • Random Processes
  • Detection and Estimation
  • Data Analysis
  • Financial Instruments and Risk Management
  • Statistics
  • Time Series Analysis
  • Numerical Solutions to Differential Equations.

Suggested Electives

  • MG 201 Economics, MG 265 Data Mining, MG 221 Applied Statistics, MG 226 Time Series Analysis and Forecasting, MG 258 Financial Instruments and Risk Management.
  • PH 204 Quantum Mechanics, PH 202 Statistical Mechanics, Electromagnetic theory, Computational Photonics, Basics of Quantum Information.
  • BC 302 Current Trends in Drug Discovery, Applied Bioinformatics. 
  • ME 286 Numerical methods for partial differential equations. 
  • EC 201 Theoretical and Mathematical Ecology, EC 303 Spatial and Stochastic Dynamics in Biology.
  • NE 250 Entrepreneurship, Ethics and Societal Impact.
  • Stochastic Approximation, Random Topology and Geometry, Topological Data Analysis. 
  • Machine Learning for Geosciences, Data Analytics, Applied Statistics, Computational Epidemiology, Foundations of Robotics, Computational Robotics, Computational Physics, Multivariate Analysis for Machine Learning, Mathematical Biology, Linear and Integer Programming, Network Optimization.


  • Sem 7 & 8: Independent Study/Research Experience project (ISP)* (6 credits). 
  • Sem 8: Research/Industry project* (12 credits).
  • Project availability is subject to the student finding a supervisor.
  • Faculty or Research Groups can advertise projects and students can apply and be selected.
  • Students will be encouraged to carry out project work in the industry.

Course Instructors

August 2023


UMA 101 Analysis and Linear Algebra I 4:0 Gautam Bharali
UENG 101 Algorithms and Programming 3:1 Viraj Kumar, Y Narahari
UH 101 Ways of Knowing 2:0 Nitin Mayanath
UBL 101 Introductory Biology I 3:1Sumanta Bagchi, Jayanta Chatterjee
UCY 101 Physical Principals of Chemistry 3:1Anshu Pandey, V Kaliginedi, Chinmoy Ranjan
UPH 101 Introductory Physics I 3:1Banibrata Mukhopadhyay, Victor S. Muthu, Binita Tongbram, Animesh Kuley


UMA 201 Probability and Statistics4:0 Manjunath Krishnapur
UMC 201 Data Structures and Algorithms3:1 C. Pandu Rangan
UMC 202 Numerical Methods3:1 Thirupathi Gudi
UH 201 Ways of Doing2:0 A Indira
UBL 201 Introductory Biology III3:1Dipankar Nandi, Tanveer Hussain , Ashesh Dhawale , Arnab Barik
UCY 201 Basic Organic Chemistry 3:1Akkattu T Biju, Durga Prasad Hari
UPH 201 Introductory Physics III3:1Arindam Ghosh , Baladitya Suri
UENG 201 Materials Engineering3:0Abinandanan T A

August 2022


UMA 101 Analysis and Linear Algebra I 4:0 Purvi Gupta
UENG 101 Algorithms and Programming 3:1 Viraj Kumar, Y Narahari
UH 101 Ways of Knowing 2:0 Aparna C, Nitin M
UBL 101 Introductory Biology I 3:1Rohini Balakrishnan, Jayanta Chatterjee
UCY 101 Physical Principals of Chemistry 3:1Anshu Pandey, Atanu Bhattacharya, Chinmoy Ranjan
UPH 101 Introductory Physics I 3:1Banibrata Mukhopadhyay, Jaydeep Kumar Basu, Binita Tongbram

January 2023


UMA 102 Analysis and Linear Algebra II 4:0 Ved Datar
UENG 102 Electrical and Electronics Engg 3:1 Kaushik Basu
UMC 102 Computer Systems 3:0 Matthew T. Jacob
UBL 102 Introductory Biology II 3:1Dipshikha Chakravortty, Sachin Kotak, Arun Kumar
UCY 102 Basic Inorganic Chemistry 3:1Debasis Das, Partha Sarathi Mukherjee, Sandya Sukumaran
UPH 102 Introductory Physics II 3:1Srimanta Middey, Ramesh K, Prasad V Bhotla
UENG 103 Earth and its Environment 3:0Ramananda Chakrabarti, Sambuddha Misra, Prasenjit Ghosh, Sajeev Krishnan
UMC 103 Discrete Mathematics 3:0 C. Pandu Rangan
UH 102 Ways of Seeing 2:0 Nakula Somana, Mahesh Pattar