This is an introductory calculus-based engineering physics course covering topics in mechanics and thermodynamics.  Topics include:  Use of vectors in mechanics, force and motion, free-body diagrams, work and energy, potential energy and conservation of energy, momentum and impulse, torque, essential ideas in rotation, equilibrium, gravitation, ideal fluids, oscillations, waves and sound, elementary concepts of thermodynamics and heat transfer mechanisms.  Contents will be supplemented by discussions on applications relevant to engineering.  The course is suitable for Engineering students with HKDSE physics or Combined Science with a physics component, or with permission of instructor.

This course is designed for engineering students who need to acquire skills in calculus as a crash introduction to the mathematics used in engineering.  The course emphasizes on the technique of computation without theoretical discussion.  Students are expected to have mathematics background equivalent to HKDSE with Extended Module I or II.

This is an introductory calculus-based engineering physics course covering topics in electromagnetics, optics and modern physics. Topics in electromagnetics include: electric and magnetic properties, Coulomb’s law, Gauss’ law, electromagnetic energy and forces, Biot-Savart law, electromagnetic fields and Maxwell’s equations, propagation of plane electromagnetic waves. Topics in optics include: optical interference, interferometers, optical diffraction. Topics in modern physics include: wave-particle duality, momentum and energy of photons and electrons, electronic states and energy bands, electrical conduction in metals and semiconductors. Contents will be supplemented by discussions on applications relevant to engineering. (Not for students who have taken ENGG2520, ESTR2006 or ESTR1003.)

Probability theory: population, sample spaces and events, counting, axioms of probability, conditional probability, Bayes' Theorem, discrete distributions, continuous distributions, joint distributions, expectation and decision making, random processes. Statistical inference: sampling distributions, point estimation, confidence interval, hypothesis testing, chi-square goodness-of-fit test. Introduction to regression analysis: linear regression. (Not for students who have taken ESTR2002 or ESTR2005 or ENGG2430.)