This revised syllabus introduced in July-2023 offers the MACHINE LEARNING course
for three semesters during the M.Sc. (Scientific Computing) programme.
This syllabus is NEP 2020 compliant.
M.Sc. - Scientific Computing - Syllabus
Semester I
Programming Languages and principles I
Software Engineering
Mathematics for Scientific Computing
Research Methodology for Scientific Computing
Linear Algebra
Computational Laboratory I (Project)
Semester II
Programming Languages and Principles II
Operating System Concepts
CBCS Elective - I
Numerical Computing - 1
Computational Laboratory II (Continued from Sem I)
Apart from the electives listed here M.Sc. (Scientific Computing) students can take
elective's from M.Sc./MCA - Computer Science, M.Tech - Modeling & Simulation, etc.
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
