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Industrial Mathematics Course Description

Level II – Semester I
IMT211β: Classical Mechanics-III (Fluid Dynamics) (30 lecture hrs + 15 tutorial hrs) -(Credit Value 2.5)
Equations of stream lines, Equations of vortex lines, Differentiation following the motion of a fluid. Equations of continuity, Euler’s and Bernoulli’s equations, Irrotational motion, uniqueness theorem, Kinetic energy, Sources and sinks, Images, Potential flow, Complex potential.
Method of assessment: end of semester examination

IMT2b2β : Mathematical Computing (15 lecture hrs + 60 practical hrs) -(Credit Value 2.5)
Introduction to the computer package Mathematica, how to type mathematics, special characters, basic constructions, Numerical computations, Standard functions, Accuracy, The use of variables, Working with whole numbers, Finding prime numbers, Handling algebraic expressions and doing symbolic computations, Graphics in Mathematica, Calculus in Mathematica, Solving equations, Introduction to programming (procedural vs functional) using various numerical and algebraic methods to solve equations and sets of equations, Defining new functions, Writing new commands which perform more complicated tasks.
Method of assessment: Practical examination/assignments/project report and end of semester oral examination and/oral presentation.

Level II – Semester II
IMT221β:Mathematical Modelling-II (30 lecture hrs + 15 tutorial hrs) -(Credit Value 2.5)
Introductory Numerical Solutions of Differential Equations, Mathematical Modelling through Difference Equations, Further Study on Systems of Differential Equations with Matrices. Modelling with Partial Differential Equations (PDEs): The concept of a PDE, Method of separation of variables, Mass-Balance equation (The first method of obtaining PDE Models), Momentum-Balance Equation (The second method of obtaining PDE Models), Variational Principles (The third method of obtaining PDE Models), Probability Generating functions (The fourth method of obtaining PDE Models), Nature of PDEs Initial and Boundary Conditions.
Method of assessment: end of semester examination

IMT222β: Applied Analysis (30 lecture hrs + 15 tutorial hrs) -Op. for students following Industrial Mathematics (Credit Value 2.5)
The fundamental notion of periodicity and bifurcation. The concepts of chaos and strong chaos for functions of one variables Fractals, Fractal dimensions, Julia sets and Manderbolt sets.
Method of assessment: end of semester examination

IMT223β: Applied Probability(Information Theory)(30 lecture hrs + 15 tutorial hrs) -Op. for students following Industrial Mathematics ( Credit Value 2.5)
Event Spaces, probability measure, probability space, sample space, continuity of a probability measure, Defining random variables on probability spaces and their functions, partition theorem, conditional probabilities, Distribution Functions, The law of large numbers, Introduction to Information theory and Claude Shannon’s remarkable work on mathematical formulation of the central problem in telecommunication channels, Error correcting codes for binary symmetric channel and their performances, Shannon’s noisy channel coding theorem, probability and entropy, entropy and mutual information, convex functions and Jensen’s inequality, the data processing theorem, Discrete memoryless channels and their capacity-cost functions, measuring the information content of an ensemble,the Source-Channel Coding Theorem for the Binary Symmetric Channel.
Method of assessment: end of semester examination

IMT224β: Applied Statistics I (30 lecture hrs + 15 tutorial hrs) -Op. for students following Industrial Mathematics (Credit Value 2.5)

Collecting and Summarizing data: Constructing tables and graphs, Measures of center of a set of observations, Median, Arithmetic Mean, Mode.
Samples and Populations: Methods of choosing a sample, Measures of variability: Range, Mean deviation, Variance and Standard deviation, Semi-interquartile range, five number summaries, Box and Whisker plots, stem and leaf plots.
Joint distributions of data: The Scatter diagram, the concept of a statistical relation, Quantitative description of a statistical relation, Covariance, Correlation coefficient
Linear regression: Regression equation, Prediction and error, Interpreting regression. Statistical Applications with probability models: Bernoulli, Binomial, Poisson, Normal approximations, Statistical software packages.
Method of assessment: end of semester examination