Cyprus International University Course Portal

This course integrates classical water resources engineering with sustainability concepts. It explores hydrological processes, surface and groundwater systems, water demand and supply, and the influence of climate change and human activities on water systems. Emphasis is placed on developing engineering solutions that are technically sound, environmentally responsible, and adaptable to future uncertainties.

The aim of this course is to teach the civil engineering students: the new and special concrete materials and applications.  

This course introduces the principles and applications of water resources engineering, focusing on how water moves, is stored, and can be managed in natural and human-made systems. Students will learn how to analyze catchments, estimate runoff, and design systems for water supply, irrigation, and flood control.

The course combines theory with practical tools such as HEC-HMS for hydrologic modeling and decision-making. It also emphasizes sustainable management, showing how engineering solutions connect with environmental and social needs. By the end of the course, students will understand the main components of the hydrologic cycle, evaluate water demands, and apply engineering approaches to solve real-world water challenges.

This course aims to provide graduate students with the information and practices in project appraisal, life cycle costing, value management and envirnonmental management. This course covers a wide range of subjects that are required in the daily operations in the construction industry. Students will gain valueable experience through different types of projects which will require the application of life cycle costing methods, value management, envirnonmental appraisal and management and an overall project appraisal.

This course develops the scientific and engineering skills necessary to design energy-efficient and sustainable buildings and built environments. The course aims to integrate laterally a wide range of advanced environmental building design aspects that includes building physics, enhanced natural ventilation, sustainable building materials, rational water usage, global energy demands and renewable/alternative energy technologies, bioclimatic building design, perception of human comfort, and environmental management and strategies. The course also demonstrates examples of both sustainable and unsustainable aspects of current building design practice, and how international policy frameworks can act as both drivers and barriers to sustainable solutions. The course involves individual case studies of international environmental design projects. Current sustainability certification schemes are presented and discussed critically.

This course provides a comprehensive understanding of transforming data into visuals by introducing participants to important principles of analytical design and practical data visualization techniques for the exploration and presentation of univariate and multivariate data. Data visualization is covered as one of the most effective tools to explore, understand, and communicate patterns in quantitative information. The course provides a broad understanding of techniques and algorithms of turning data into readable visuals. Upon completion of the course, students learn about data visualization processes including data modeling, data aggregation and filtering, mapping data attributes to graphical attributes, and visual encoding. Students also learn to assess the effectiveness of different visualization designs, and critically evaluate each design decision.

Bu derste, kökleri bilgisayar bilimi, yapay zeka ve istatistik olan istatistiksel makine öğrenimi işlenir ve bilgisayarların 'öğrenme' süreciyle performanslarını iyileştirmelerine, karar vermelerine ve tahmin etmelerine olanak tanıyan geniş bir algoritma anlayışı sağlanır. Derste temel yöntemler öğretilir ve gerçek verilere uygulanır. Başlıktaki istatistiksel terimi, makine öğrenimine baskın yaklaşımlar oluşturan istatistiksel teknikleri vurgular. Ders, metodolojiyi teorik temeller, hesaplama unsurları ve istatistiksel teori konularıyla bütünleştirir ve öğrencilere modern istatistiksel makine öğrenimi yöntemlerinin ardındaki temel fikirleri ve sezgileri sağlar. Bu dersi tamamlayan öğrencilerin, konuşma tanıma, internet arama, biyoinformatik, görüntü ve ses sinyali analizi, veri madenciliği ve keşifsel veri analizi konularında denetimli ve denetimsiz öğrenme yaklaşımlarını öğrenmeleri beklenmektedir.

In this course, statistical machine learning which has roots in computer science, artificial intelligence and statistics is covered and a broad understanding of algorithms that allow computers to improve their performance through the process of ‘learning’ and enable them to make decisions and predictions is provided. Fundamental methods are taught and applied to real data. The term statistical in title emphasizes the statistical techniques, which form dominant approaches to machine learning. The course integrates methodology with theoretical underpinnings, computational elements, and statistical theory issues. By completion of this course, students are expected to learn about supervised and unsupervised learning approaches to speech recognition, internet search, bioinformatics, image and audio signal analysis, data mining and exploratory data analysis.