Search results: 2027
This course will provide extensive discussions for the design and analysis of retaining systems including gravity walls, cantilever walls, mechanically stabilized earth walls, sheet-pile walls, and diaphragm walls. Students will learn to use computer software to analyze a retaining wall for deep excavations. The course will cover the following topics: lateral earth pressures, retaining wall types, analysis of backfilled walls and in-situ walls, stability of wall and base, settlements due to excavation, strut and anchor systems. Basic concepts of theory of earth pressures behind retaining structures, with special application to design of retaining walls, bulkheads, sheet piles and excavation bracing.
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.
- Teacher: Omer Damdelen
Construction contractors are realizing that safety, productivity, and quality are inextricably linked and are moving to implement programs that go beyond regulatory compliance and take a more active stance towards protecting their employees in the field. In this course, instructor discusses the most common types of workplace injuries, along with measures that can be taken to prevent them. Instructor also covers safety and health management systems, highlighting the importance of safety as something that must be actively managed in conjunction with quality and productivity. Plus, s/he goes over the role the construction manager needs to play in this process.
Construction projects are risky. Things can and do go wrong
regularly. In this course, we explore the fundamentals of risk
management in construction projects. The purpose of this course is to
learn the tools and techniques to manage uncertain events and
circumstances that can influence the project. The course is designed for
engineers, quantity surveyors, and construction management
professionals looking to further develop their learning.
By the end of this course, you should be able to:
Understand the fundamentals of risk management including key terms and definitions
Apply and use the risk management process to properly identify, analyze and manage uncertain events and circumstances
Understand
the specific risks applicable to construction projects including
technical, construction, health and safety, environmental, commercial,
and external
This course focuses on learning data science through the interest to development and improvement of capability of solving rich problems from data point of view in a systematic and principled way by using high quality instructions and basic level data science techniques. Students will be introduced to what data science is, will discover the applicability of data science across fields, and will learn how data analysis can help them make data driven decisions. Students will gain familiarity with various open source tools and data science programs used by data scientists, like Jupyter Notebooks, RStudio, GitHub, and SQL. This course provides the students with the required structure and responsibilities in order to educate them as data scientists progressing a right way with high concluding capabilities.
- Teacher: Gorkem Akdur
- Teacher: Kian Jazayeri
- Teacher: Daniel Okpala
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.
This course looks into internet and global network concepts are taken up in detail. This course also deals with the most popular topics such as the history of Internet, a general overview of the internet based opportunities and applications (such as e-mail, internet browsers, file transfer opportunities, list drivers, etc.) internet based research and information resources, the global network services, creation of web pages using HTML. A history of the technologies appeared upon development of internet and an overview of the mentioned technologies together with the methods of utilization of these technologies for personal and business purposes is provided to the students.
- Teacher: Andre Sena
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.
- Teacher: Yasemin Bay
Data mining, which is the study of algorithms and computational paradigms that enable computers to search datasets for patterns and regularities and make predictions and forecasts is covered in this course. Knowledge discovery is introduced comprehensively. The course explores data selection, cleaning, coding, the application of various statistical and machine learning approaches, and visualization of the resulting structures, which are all steps in knowledge discovery. Students who successfully complete this course are supposed to learn about several data mining techniques, including classification, rule-based learning, decision trees, and association rules. Additionally, students are expected to learn about selection and cleaning of data, machine learning methods for "learning" about "hidden" patterns in data, and reporting and visualizing the resulting knowledge.
- Teacher: Kian Jazayeri
This course covers big data, which becomes increasingly available with digitalization of many aspects of human life and with increasingly growth of social media platforms. The course presents a broad understanding of big data and different approaches of big data manipulation. Students will explore how terabytes and petabytes of storage space can be occupied by big data. In this course, the Hadoop software, the Hadoop Distributed File System (HDFS) and the MapReduce technique as well as supervised and unsupervised machine-learning approaches are introduced and artificial neural networks are thoroughly investigated. By completion of the course, students are expected to be able to harvest big data sets from the internet and process by using different methods and tools.
This course explores the many ethical dilemmas that arise in the contemporary practice of data science and creates a framework for comprehending these concerns. The impact of unethical behavior and how data are used ethically in society will be covered. The Findable, Accessible, Interoperable, and Reusable (FAIR) data principles are described. By the end of the course, students will be able to recognize and carefully differentiate between problems unique to data science and those that arise as a result of using other related computational methods of analysis, such as machine learning and artificial intelligence. They will also be able to distinguish between ethical misconduct unique to data science methods and those that may result from the contexts in which these methods are used.
- Teacher: Sara Salehi
In this course Artificial Neural Network (ANN), which is a mathematical paradigm imitating biological neural network for reasoning
and problem solving is covered. With focus on practice, the course cover ANN models for various real-world applications and offers a
hands-on introduction to deep learning tools and techniques. Students do not need to have an extensive math background to
understand this course. The course covers McCulloch-Pitts model and basic neural network models, multilayer perceptron,
associative memory, self-organizing feature maps, recurrent neural networks, etc. and reviews applications of these models to
various types of data. Upon completion of this course, students will gain a broad understanding of the context of neural networks
and deep learning, the data demands of deep learning and the parameters for neural networks.
- Teacher: Tolgay Karanfiller