Basic concepts of probability theory. Random variables. Probability distributions. Averages. Moments. Characteristic functions. Independence. Discrete distributions: Binomial & Poisson Continuous distributions: Uniform, exponential & Gaussian. Introduction to statistical inference and its applications in engineering. Statistics of measurements. Confidence intervals. Regression (least squares fitting of data) & correlation

Course Objective: to develop further the mathematical skills necessary to analyze, model and solve engineering system problems. Course content: Linear Algebra : Operations on determinants and matrices , basic concepts , addition of matrices , transpose of matrices , Inverse of matrices , matrix multiplication, system of linear equations , Gauss elimination , rank of matrices, existence and general properties of solution, , Cramer's rule, bilinear , quadratic , hermitian, skew- hermitian forms, eigenvalues and eigenvectors, eigenvalue of hermitian ,skew – hermition and unitary matrices. Vector spaces , sub- spaces , dependence and independence , rank and dimension, eigenvalues and eigenvectors problem , Cayley – Hamilton theorem. Scalar and vector field theory Vector in 3- space , algebra of vectors , gradient , divergence , curl , curves , arc length , tangent , normal , binormal , curvature , torsion.

EE204 – Advance Programming (3 units) 

 Introduction: general overview of the C programming language, its applications, and a brief history. Structure of C Programs: C character set, program layout, preprocessor directives, commenting, and basic structure of a C program. Data Types and Operations: usage of integer, float, and character variables; arithmetic operations, assignment statements, and expression evaluation. Input/Output Functions: use of scanf() and printf(), format specifiers, and string handling. 9 Control Structures: implementation of control loops (while, do-while, for), logical conditions, and nesting. Conditional Execution: structures such as if, if-else, and switch; use of break, continue, and basic loop logic. Functions and Modularity: definition and invocation of functions, return values, parameter passing, and scope. Arrays and Strings: one-dimensional and multi-dimensional arrays, string operations and manipulations. MATLAB Integration: introduction to MATLAB for numerical computing and matrix operations; basic syntax, script writing, and comparison with C in terms of functionality for scientific and engineering tasks. Practical Work: hands-on exercises and projects in both C and MATLAB to reinforce programming logic, problem solving, and computational thinking.

EE202 Electric Circuit I (3units) 

 This course covers the fundamental concepts of electrical circuits, including current, voltage, and resistance, Ohm’s and Kirchhoff’s laws for analyzing current and voltage in electrical networks, circuit analysis methods using nodal and mesh techniques, applications of Thevenin’s and Norton’s theorems for circuit simplification, study of capacitors and inductors and their effects on circuits, time-domain response analysis of RC and RL circuits, the concept of mutual and self-inductance and their applications in electrical circuits, an introduction to Fourier analysis for periodic signals, and its applications in electrical systems

 Introduction, electrical structure of materials, types of materials, types of bonding between atoms: ionic, covalent and metallic bonds, electrical conductivity: conductivity versus temperature, conductivity and second Ohm's law, charge carriers, mobility, atomic energy levels and bands, conductor materials, resistivity, resistivity versus temperature, super conductivity, semiconductors: intrinsic semiconductor, semi conductivity versus temperature, extrinsic semiconductor, doping,( N-type , P- type ), 7 P-N junction, insulators or dielectric materials: Polymeric, Ceramics, polarization calculations, dipole moment, dielectric constant, magnetic materials, properties of permanent magnets, soft and hard magnets, electromagnetic materials .