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
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