Search results: 1876
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: Labaran Isiaku
- Teacher: Kian Jazayeri
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
The concepts of data science will be covered throughout the course from a variety of angles, including conceptual formulation and properties, solution algorithms and their applications, data visualization for exploratory data analysis, and the appropriate presentation of modeling outcomes. With the use of real-world examples, students will understand the purpose, effectiveness, and constraints of models. Upon completion of the course, students will be able to comprehend the contemporary data science landscape and technical terminology, identify key concepts and tools in the field of data science and determine when they can be applied effectively. Students will also be able to recognize the significance of curating, organizing, and wrangling data, explain uncertainty, causality, and data quality and anticipate the effects of data use and misconduct.
This course covers the algorithmic techniques and approaches required to handle various types of structured, semi-structured and unstructured data. The goal of the course is to teach algorithmic methods that serve as the cornerstones for handling and analyzing large datasets in a variety of formats. The course specifically covers how to pre-process big datasets, store big datasets effectively, design quick algorithms for big datasets, and evaluate the performance of designed algorithms. Algorithms for sorting, searching and matching as well as graph and streaming algorithms will be introduced. Upon completion of this course, students will have a broad knowledge of different algorithms for pre-processing, organizing, manipulating and storing different data types. Students will also be able to carry out performance analysis of each algorithm.
- Teacher: Kian Jazayeri
Makine öğrenimi kavramı ve altında yatan olasılıksal ve istatistiksel yaklaşımlar bu derste baştan sona geniş çaplı bir şekilde ele alınmaktadır. Veri bilimi için makine öğrenimi kavramı çok değişkenli veri analizi için gerekli araç ve tekniklere odaklanılarak kapsamlı bir şekilde gözden geçirilmektedir. Bu dersi alan öğrencilerin temel matematik ve Phyton programlama altyapısına sahip olmaları beklenmektedir. Bu dersi tamamlayan öğrenciler belirsiz verileri olasılıksal modeller ile ele almak için gerileme, sınıflandırma, kümeleme, boyutluluk indirgeme ve değerlendirme yöntem ve tekniklerini uygulama becerilerine sahip olacaklardır. Bu dersi tamamlayan öğrenciler, deneme yanılma yoluyla kendi kurallarını ortaya koyarak problem çözebilen sistemleri, verilerdeki örüntüleri otomatik olarak tanımlayan sistemlerle karşılaştırabileceklerdir.
In this course, the concept of machine learning and its underlying probabilistic and statistical approaches will be covered extensively with a focus on the tools and techniques required for multivariate data analysis. Students taking this course are expected to have basic mathematics and Python programming background. Students completing this course will have the skills to apply regression, classification, clustering, dimensionality reduction and evaluation methods and techniques to handle uncertain data with probabilistic models. Upon completion of this course, students will be able to compare systems that can solve problems by coming up with their own rules via trial and error with systems that automatically identify patterns in data.
- Teacher: Yasemin Bay
This course provides an understanding of numerical mathematic applications in data science. The floating-point representation of real numbers, truncation and round off errors, iterative approaches, and convergence are some of the main points in numerical mathematics that are covered in this course. Students will study the most basic and crucial algorithms for the fundamental numerical mathematics problems, such as the solution of algebraic equations, numerical estimation of derivatives and integrals, solution of differential equations, approximation of functions by polynomials and Fourier series and solution of systems of linear algebraic equations. Upon completion of this course, students will be able to formulate and solve problems using mathematical methods and tools, identify, understand, and solve algebraic equations and develop experience with numerical and symbolic mathematical software.
- Teacher: Tolgay Karanfiller