CCMAS Course Search

Forces in nature. Electrostatics; electric charge and its properties, methods of charging. Coulomb’s law and superposition. electric field and potential. Gauss’s law. Capacitance. Electric dipoles. Energy in electric fields. Conductors and insulators, current, voltage and resistance. Ohm’s law and analysis of DC circuits. Magnetic fields. Lorentz force. Biot-Savart and Ampère’s laws. magnetic dipoles. Dielectrics. Energy in magnetic fields. Electromotive force. Electromagnetic induction. Self and mutual inductances. Faraday and Lenz’s laws. Step up and step-down transformers: Maxwell's equations. Electromagnetic oscillations and waves. AC voltages and currents applied to inductors, capacitors, resistance, and combinations.

These introductory courses emphasise quantitative measurements. the treatment of measurement errors. and graphical analysis. A variety of experimental techniques should be employed. The experiments include studies of meters. the oscilloscope. mechanical systems. electrical and mechanical resonant systems. Light, Heat, viscosity and many others. covered in PHY 101 and PHY 102. However. emphasis should be placed on the basic physical techniques for observation. measurements. data collection. analysis and deduction.

This practical course is a continuation of PHY 107 and is intended to be taught during the second semester of the 100 level to cover the practical aspect of the theoretical courses that have been covered with emphasis on quantitative measurements. The treatment of measurement errors, and graphical analysis. However, emphasis should be placed on the basic physical techniques for observation, measurements, data collection, analysis and deduction.

The theory and practices of Human-Computer Interfaces (HCI) from information systems angle would be discussed. Various industry and web HCIs designs will be evaluated from the user- centered perspective utilising information systems. Rapid prototyping and comparative evaluation of design are essential parts of this course. In addition, users’ expectation of interface design, interaction and usability components and user-centered HCI evaluation and development will be covered.

This is an introductory course on Artificial Intelligence. The topics include; Overview: foundations, scope, problems, and approaches of AI. Intelligent agents: reactive, deliberative, goal-driven, utility-driven, and learning agents; Artificial Intelligence programming techniques. Problem-solving through Search: forward and backward, state-space, blind, heuristic, problem-reduction, A, A*, AO*, minimax, constraint propagation, neural, stochastic, and evolutionary search algorithms, sample applications. Knowledge Representation and Reasoning: ontologies, foundations of knowledge representation and reasoning, representing and reasoning about objects, relations, events, actions, time, and space; predicate logic, situation calculus, Content logics, reasoning with defaults, reasoning about knowledge, sample applications. Planning: planning as search, partial order planning, construction and use of planning graphs. Representing and Reasoning with Uncertain Knowledge: probability, connection to logic, independence, Bayes rule, bayesian networks, probabilistic inference, sample applications. Decision-Making: basics of utility theory, decision theory, sequential decision problems, elementary game theory, sample applications. Machine Learning and Knowledge Acquisition: learning from memorisation, examples, explanation, and exploration. learning nearest neighbor, naive Bayes, and decision tree classifiers, Q-learning for learning action policies, applications. Sample Applications of AI, student project presentations.

Description foundational concepts of biostatistics. Introduction to application of basic theoretical concepts of biostatistics in health science, epidemiology, and public health. Knowledge of measurement scales, types of variables and data along with various statistical methods for summarising and presenting different types of data will be provided. Introduction of the concept of probability with its applications in epidemiology and public health. Moderate level presentation of important probability distributions applied commonly in epidemiology and public health.

Brief history of computing. Description of the basic components of a computer/computing device. Input/Output devices and peripherals. Hardware, software and human ware. Diverse and growing computer/digital applications. Information processing and its roles in society. The Internet, its applications and its impact on the world today. The different areas/programs of the computing discipline. The job specialisations for computing professionals. The future of computing. Lab Work: Practical demonstration of the basic parts of a computer. Illustration of different operating systems of different computing devices including desktops, laptops, tablets, smart boards and smart phones. Demonstration of commonly used applications such as word processors, spreadsheets, presentation software and graphics. Illustration of input and output devices including printers, scanners, projectors and smartboards. Practical demonstration of the Internet and its various applications. Illustration of browsers and search engines. How to access online resources.

Introduction to data science, using R and R Studio; R language syntax. Writing of R code, learn about R data-types and data-structures, exploring data and produce plots. How to use R for analysing experimental data using simple statistical techniques like t-tests, analysis of variance and linear regression. Application of the techniques with R. Cleaning up datasets to creating interactive and reproducible reports with transferable skills that would apply to any scientific or business domain.