This revised syllabus introduced in July-2018 offers the MACHINE LEARNING course for three semesters during the M.Sc. Scientific Computing programme.

Masters in Scientific Computing - Syllabus

    Semester I

  • SC-101 Programming Languages and principles I
  • SC-102 Software Engineering
  • SC-103 Foundation of Scientific Computing I
  • SC-104 Foundation of Scientific Computing II
  • SC-105 Computational Laboratory I

    Semester II

  • SC-201 Programming Languages and Principles II
  • SC-202 Operating System Concepts
  • SC-203 Elective
  • SC-204 Numerical Methods for Scientific Computing I
  • SC-205 Computational Laboratory II

    Semester III

  • SC – 301 Network Concepts
  • SC – 302 Parallel Computing.
  • SC – 303 Elective course
  • SC-304 Numerical Methods for Scientific Computing II
  • SC-305 Elective

    Semester IV

  • SC - 401 R & D/Industrial Project

Detailed Semester Wise Syllabus [Download]

Electives

  • EL-1 : Parallel Computing and Grid Computing
  • Introduction Solving Problem in parallel Structure of parallel computers Programming parallel computers Case Studies Grid Computing
  • EL-2 : Application of Computers to Chemistry
  • Computational Chemistry, Fundamentals of Chemistry, Molecular Representations and Search Molecular Graphics and fitting Force Field (FF) Methods Classical energy minimization techniques Conformational Analysis, Semi-empirical QM calculations Molecular Docking Molecular Descriptors Quantitative Structure Activity, Relationship Futuristic modeling techniques
  • EL-3 : Statistical Computing
  • Introduction to statistical computing, Random Number Generation, Monte Carlo Methods, Non-linear Statistical Methods, Multiple Linear Regression Analysis
  • EL-4 : Computer Application if Physics
  • Monte Carlo Methods, Numerical Solutions of Schrodinger equations, Electronic Structure Calculation on simple solids, Classical Molecular Dynamics
  • EL-5 : Biological Sequence Analysis
  • Analysis of DNA and Protein sequence, Sequence alignment, Fragment assembly, Genome sequence assembly, Neural network concepts and secondary structure prediction Probabilistic models, Evolutionary analysis
  • EL-6 : Modeling of Biological Systems
  • Concepts and principles of modeling. Limitations of models, Models of behavior, Modeling in Epidemiology and Public Health SIR models
  • EL-7 : Artificial Intelligence
  • Introduction to Artificial Intelligence Game playing Knowledge representation using predicate logic Knowledge representation using non monotonic logic Planning Perception Learning Neural Networks Natural language processing Expert system
  • EL-8 : Soft Computing
  • Fuzzy logic, Neural Networks, Genetic Algorithms
  • EL-9 : Design Concepts and Modeling
  • Introduction to design process, Inception phase, Elaboration phase, Construction phase, Transition phase
  • EL-10 : Software Testing
  • Introduction to software testing and analysis, Specification-based testing techniques, Code-based testing techniques, Unit testing, Integration testing, OO-oriented testing, Model-based testing, Static analysis, Dynamic analysis, Regression testing, Methods of test data generation and validation, Program slicing and its application, Reliability analysis, Formal methods; verification methods; oracles, System and acceptance testing
Announcements and Updates

M.Sc. (Scientific Computing) Second Merit List

M.Sc. Scientific computing first year lectures to start from Thursday 8th Aug. 2019. NEW
Schedule for 8th Aug.
Reporting time 10:15 AM
10:30 - 11:30 AM : Orientiation Session
11:30 AM - 1 PM : SC-101 PPL-I Lecture
1 - 2 PM : Break
2 - 4 PM : Lab Session

M.Sc. (Scientific Computing) - Fees Undertaking