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Use TensorFlow to take machine learning to the next level. Your new skills will amaze you.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Python (Computer program language) Machine learning
- Resource Type:
- Others
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MOOC
The building industry is exploding with data sources that impact the energy performance of the built environment and health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole analytics tool for professionals in this field. Participating in mainstream data science courses might provide skills such as programming and statistics, however the applied context to buildings is missing, which is the most important part for beginners. This course focuses on the development of data science skills for professionals specifically in the built environment sector. It targets architects, engineers, construction and facilities managers with little or no previous programming experience. An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from the design, construction, and operations of buildings to learn and practice data science techniques. Essentially this course is designed to add new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and basic machine learning using the Python programming language, the Pandas data analytics and sci-kit learn machine learning libraries, and the web-based Colaboratory environment. In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Python (Computer program language) City planning -- Statistical methods Information visualization
- Resource Type:
- MOOC
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MOOC
Why are hybrid vehicles still more common than battery electric ones? Why are electric vehicles still more expensive than conventional or hybrid ones? In this course, you will get the answers to this and much more. While electric motors can improve vehicles regarding performance, energy consumption and emissions, they suffer from high cost and weight of batteries. Smart combinations of electric motors and combustion engines in a hybrid powertrain can combine these strengths with the advantages of combustion engines. This course is aimed at learners with a bachelor's degree or engineers in the automotive industry who need to develop their knowledge about hybridpowertrains. Inthis course, we willexamine different mechanical layouts of hybrid powertrains and how they influence the performance and complexity of the powertrain. Different sizing of powertrains in micro, mild, full hybrids, as well as plug-in hybrids, is also discussed and you'll learn how they can be modelled and analyzed for example by simulation of driving cycles. You will also learn about the Energy Management system and how this controls the hybrid powertrain modes and when to charge and discharge the battery. As a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Electric vehicles Hybrid electric vehicles
- Resource Type:
- MOOC
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MOOC
This course provides the tools needed to build a low-carbon power sector around the world. By diving into the perspective of different players in the power sector - from investors through to utilities, regulators and project developers - you will be able to choose the right strategies, policies and other levers needed to incentivise a cleaner power mix in your own context. This course explores the mix of approaches that can create a pro-renewables environment. It explores this from a policy, regulatory and supply-chain perspective and examines the incentives and rules available. Key policies are brought to life through case studies, learning from both success and failure. Key messages of the course include: - Ambitions for renewable electricity must be grounded in technical and financial feasibility - Pro-renewables environments recognise the needs of energy supply chain actors (e.g. project developers, utilities, regulators, electricity customers) and balances pricing, fiscal and financial and wider policies to incentivise and drive deployment - There are multiple ways to encourage deployment of renewables across different scales – these have strengths and weaknesses and must balance rate of deployment, affordability and efficiency of generation - Incentives and rules are a package and can be aligned to deliver affordable, efficient renewable electricity - several real-world examples demonstrate this - Different countries have succeeded and failed in creating pro-renewables environments – demonstrating that while lessons can be used from these experiences, there is no single route to success and the environment must be bespoke to the circumstances of the country. This course should help decision makers across the electricity supply chain, in both the public and private sector, understand what mix of incentives is ideal from their perspective.
- Subjects:
- Environmental Engineering, Building Services Engineering, and Environmental Policy and Planning
- Keywords:
- Electric power distribution -- Environmental aspects Renewable energy sources
- Resource Type:
- MOOC
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MOOC
This course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences. We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research. Given the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.
- Subjects:
- Mathematics and Statistics
- Keywords:
- Life sciences -- Statistical methods Mathematical statistics -- Data processing R (Computer program language)
- Resource Type:
- MOOC
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Courseware
If you’re interested in the concept of building with nature, then this is the engineering course for you. This course explores the use of natural materials and ecological processes in achieving effective and sustainable hydraulic infrastructural designs. You will learn the Building with Nature ecosystem-based design concept and its applications in water and coastal systems. During the course, you will be presented with a range of case studies to deepen your knowledge of ecological and engineering principles. You’ll learn from leading Dutch engineers and environmental scientists who see the Building with Nature integrated design approach as fundamental to a new generation of engineers and ecologists. Join us in exploring the interface between hydraulic engineering, nature and society.
- Subjects:
- Building Services Engineering and Hydraulic Engineering
- Keywords:
- Sustainable development Hydraulic engineering Water resources development -- Environmental aspects
- Resource Type:
- Courseware
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Courseware
For the first time in history, the number of world citizens without access to electricity services has dropped below one billion, but still more than 2.8 billion people lack access to clean and affordable cooking fuels. Access to clean, affordable and reliable energy services for all world citizens is a precondition for the achievement of many other Sustainable Development Goals, such as health and economic development. The provision of sustainable energy services for all is not just a technological challenge or one confined to developing countries. Industrial and post-industrial societies also need to address issues of energy poverty and energy injustice. Rather than tackling the technological dimension of the formidable challenge to provide an inclusive energy system with renewable and climate-neutral energy resources, this course will focus on its social and institutional dimension. Introduction to the principle of the 4 As of energy services – Accessibility, Availability, Affordability, and Acceptability (environmental and social) will enrich your perspective as an engineering professional. Balancing these four critical and interdependent criteria is a recurrent challenge for individuals and society as a whole, as the characterization of the four As evolves with economic development and changing societal preferences. You will learn how the rules of the game as defined in laws, regulation and market designs impact the balance between the 4As. Using a wider socio-technical systems perspective you will discover new solutions for the inclusive provision of energy services beyond the purely technological solutions. After this course you can engage in a richer, more informed debate about how to achieve an inclusive energy system. You will be able to translate this knowledge into strategies to serve society’s future energy needs. The cases presented from developed and developing countries will help you to develop and test your analytical skills. Interviews with industry leaders shaping the energy system will challenge you to reflect on the position these leaders take and the interests they serve. Lastly, you will put yourself to the test by demonstrating your newly acquired knowledge and skills as a strategic policy advisor, in writing guidelines for a strategic action plan for the energy system and institutional context which are relevant for you, in your company, your city or your country.
- Subjects:
- Environmental Engineering and Environmental Policy and Planning
- Keywords:
- Energy policy Sustainable development Power resources -- Economic aspects Power resources -- Environmental aspects
- Resource Type:
- Courseware
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MOOC
Autonomous vehicles, such as self-driving cars, rely critically on an accurate perception of their environment. In this course, we will teach you the fundamentals of multi-object tracking for automotive systems. Key components include the description and understanding of common sensors and motion models, principles underlying filters that can handle varying number of objects, and a selection of the main multi-object tracking (MOT) filters. The course builds and expands on concepts and ideas introduced in CHM013x: ""Sensor fusion and nonlinear filtering for automotive systems"". In particular, we study how to localize an unknown number of objects, which implies various interesting challenges. We focus on cameras, laser scanners and radar sensors, which are all commonly used in vehicles, and emphasize on situations where we seek to track nearby pedestrians and vehicles. Still, most of the involved methods are more general and can be used for surveillance or to track, e.g., biological cells, sports athletes or space debris. The course contains a series of videos, quizzes and hands-on assignments where you get to implement several of the most important algorithms. Learn from award-winning and passionate teachers to enhanceyour knowledge at the forefront of research on self-driving vehicles. Chalmers is among the top engineering schools that distinguish itself through its close collaboration with industry.
- Subjects:
- Electrical Engineering, Mechanical Engineering, and Transportation
- Keywords:
- Automobiles -- Design construction Computer vision Automated vehicles
- Resource Type:
- MOOC
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Courseware
Underestimating project complexity is widely accepted as one of the major causes of project failure. Based on international benchmarking activities (Merrow, 2010), we know that an average of 40% of projects do not deliver what they promised; for megaprojects in the oil and gas industry this figure is even worse (Ernst&Young, 2014). As with most external factors, many of the causes and consequences of complexity are difficult to avoid or control. When dealing with complexity, standard practices in the field of project management often overlook the inherent uncertainties linked to the length and scale of engineering and infrastructure projects and their constantly changing environments. The situation is exacerbated by rapidly evolving technologies and social change. Attempts to overcome these challenges by simply trying to reduce their causes is not enough. In this course, you will learn our approach to mastering complexity, focused on front-end development and teamwork, which will help you develop the skills you need to make timely actions in order to tackle complexities and improve your chances of project success. You will learn how to enhance your own capacities and capabilities by ensuring you have the necessary balance of complementary skills in your team. Project success starts with recognizing the main drivers of complexity, which can be highly subjective and highly dynamic. In this course, you will learn to identify what makes a project complex and how to perform a complexity assessment. Examining the elements of a project (such as interfaces, stakeholders, cultures, environment, technology, etc.) and their intricate interactions is key to mastering complexity. You will analyze these elements in the context of your own project. Then, based on our complexity framework, you will identify the complexity footprint of your project and use it to adapt your management processes. With personalized guidance and feedback from our world-class instructors, you will learn how to recognize what competencies you need to develop and how to adapt your management style accordingly, not only to improve project performance but also to enhance your decision-making capacity. This course has been designed by TU Delft’s international experts on Project Complexity, and is based on more than 60 years of practical experience as well as relevant research in the field. “We see projects still fail and there is a need to do things differently. That’s what this course is about: delivering the best practices for project execution based on our state-of-the-art research.” – Professor Hans Bakker.
- Subjects:
- Building and Real Estate
- Keywords:
- Construction industry -- Management Complexity (Philosophy) Project management
- Resource Type:
- Courseware
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Courseware
Are you an engineer, scientist or technician? Are you dealing with measurements or big data, but are you unsure about how to proceed? This is the course that teaches you how to find the best estimates of the unknown parameters from noisy observations. You will also learn how to assess the quality of your results. TU Delft’s approach to observation theory is world leading and based on decades of experience in research and teaching in geodesy and the wider geosciences. The theory, however, can be applied to all the engineering sciences where measurements are used to estimate unknown parameters. The course introduces a standardized approach for parameter estimation, using a functional model (relating the observations to the unknown parameters) and a stochastic model (describing the quality of the observations). Using the concepts of least squares and best linear unbiased estimation (BLUE), parameters are estimated and analyzed in terms of precision and significance. The course ends with the concept of overall model test, to check the validity of the parameter estimation results using hypothesis testing. Emphasis is given to develop a standardized way to deal with estimation problems. Most of the course effort will be on examples and exercises from different engineering disciplines, especially in the domain of Earth Sciences. This course is aimed towards Engineering and Earth Sciences students at Bachelor’s, Master’s and postgraduate level.
- Keywords:
- Observers (Control theory) Mathematical statistics
- Resource Type:
- Courseware