In this course, you will learn to design the computer architecture of complex modern microprocessors. All the features of this course are available for free. It does not offer a certificate upon completion.

Practice makes perfect. It’s true for people learning to master a new skill, and it’s also true for your AI brain. Just as you need the right environment to practice, get feedback and try again, so does your AI brain. In this course, you’ll solve industrial engineering problems inspired by real problems your instructors have worked on in industry. You’ll learn how to build, test and deploy an AI brain using Microsoft Bonsai, a cloud-based, low-code platform. We’ll walk through the entire Bonsai platform from setup to deployment. Along the way, you’ll use Bonsai to conduct machine teaching experimentation to train a brain and assess its progress. Because you’ll be teaching the brain a relatively complex task, you’ll run multiple simulations until you’re satisfied with the results. You’ll then prep the brain for graduation into the real world — deploying it into a machinery control system or other live environment. At the end of this course, you’ll be able to: • Build an autonomous AI that combines reinforcement learning with machine learning, expert rules and other methods that you’ve used in the first two courses of the specialization • Establish requirements for a simulated environment for your brain to practice a task • Validate and assess your brain’s performance of a task and make improvements to your brain design • Evaluate whether a simulator is a good practice environment • Deploy a brain on a real piece of hardware This course requires an Azure subscription. This course is part of a specialization called Autonomous AI for Industry, which will launch in early 2023.

Hello and welcome to this course! The NPAP - Medical Applications of Accelerators is one out of three courses in the Nordic Particle Accelerator Program (NPAP). Here you will be taken on a tour focusing on the medical applications of particle accelerators. You will see that there are two very important, but different, applications of accelerators in hospitals. The first application concerns radiotherapy of tumours and the other concerns the production of medical nuclides for diagnosis and treatment. Both will be included in this course and described through four modules. The first module offers the basic principles of radiotherapy from a medical and physics point of view. You there learn about the main components of the machines used for radiotherapy and get to know why radiotherapy is important for cancer treatments. The second module guides you through the different types of linear accelerators used in the machines for radiotherapy. It also describes the design of the treatment head. The design is important because it is the settings of the treatment head that determines the dose and the radiated region. It is also in the treatment head where the dose given to the patient is measured. In the third module you are introduced to proton therapy. In this type of therapy protons are first accelerated and then guided down to the tumour by magnets. The machines are considerably larger and more expensive than machines used for radio therapy. The module also offers a description and comparison between different types of accelerators, and explains how the protons interact with tissue. Also ions that are heavier than protons can be used in cancer therapy. This is described in the fourth module, where we also introduce you to the production of medical nuclides. You learn how the nuclides are produces in proton and ion accelerators and how the nuclides come into play at different places in hospitals. Medical nuclides are for instance used in Positron Electron Tomography, PET. Enjoy!

Learn how aeroecology, a discipline that studies airborne life forms, has been revolutionised with the use of radar with this unique course. This course is designed to help scientists, researchers as well as ecology enthusiasts to develop skills in using radar to explore biodiversity. You will explore the origins and evolution of radar from a military technology to a powerful tool with multiple scientific applications, including aeroecology. You will then be introduced to studying global biodiversity trends and learn how to evaluate traditional methods and emerging technologies used by scientists to monitor the natural world. You will then delve deeper to understand how radar science can be used to measure and monitor biodiversity and evaluate its advantages over existing methods of biodiversity measurement. Through real life case studies, you will learn how to interpret data visualisations and radar data output, how to quantify the biomass of species, and you will discover the taxonomic limits of the technology. By the end of the course, you will have explored how this new field of study can be used to transform biological and agricultural research as well as inform environmental regulation and policy.

Have you ever viewed a news report depicting the aftermath of a devastating natural disaster? The damage to human life and property are both staggering and heartbreaking. All parts of the world face the possibility of floods, hurricanes, tornados, fires, landslides, earthquakes, tsunamis, and other natural phenomena. Are you prepared if disaster would strike you? This course will help you prepare! The course is appropriate for any learner who is proactive about developing the core competencies of disaster readiness and survival planning. It is especially useful if you are seeking techniques that can ensure your personal protection, as well as the safety of your family, property, and belongings, during a natural disaster. In addition, it offers essential preparation for a variety of emergency situations and inconveniences, even if you do not live in major tornado, flood, hurricane, tsunami, or earthquake zone. For instance, could you and your loved ones manage without access to potable water, electricity, fuel, and banking facilities? If you are unsure of your ability to respond in any of these possible scenarios, this course is for you! Throughout the course, you will be introduced to the Disaster Cycle, specifically the Mitigation and Recovery phases, and will create an extensive personal preparedness plan for survival in the absence of common amenities, such as food and water, shelter, and communication. You will also acquire practical, easy-to-apply strategies for maintaining a healthy attitude during disaster which can allow you to remain calm, avoid panic, and draw upon inner and outer resources in dire circumstances. Although death may be an inevitable outcome of extreme circumstances, a balanced outlook can provide comfort for all parties involved. Finally, issues of how institutions and governments can aid in disaster are also discussed. If you are interested in this topic you may be interested in other online programs at the University of Pittsburgh School of Nursing. Learn more about those programs by visiting our website: http://www.online.pitt.edu/programs/school-of-nursing/

This course can also be taken for academic credit as ECEA 5703, part of CU Boulder’s Master of Science in Electrical Engineering degree. This course covers the analysis and design of magnetic components, including inductors and transformers, used in power electronic converters. The course starts with an introduction to physical principles behind inductors and transformers, including the concepts of inductance, core material saturation, airgap and energy storage in inductors, reluctance and magnetic circuit modeling, transformer equivalent circuits, magnetizing and leakage inductance. Multi-winding transformer models are also developed, including inductance matrix representation, for series and parallel structures. Modeling of losses in magnetic components covers core and winding losses, including skin and proximity effects. Finally, a complete procedure is developed for design optimization of inductors in switched-mode power converters.   After completing this course, you will: - Understand the fundamentals of magnetic components, including inductors and transformers - Be able to analyze and model losses in magnetic components, and understand design trade-offs  - Know how to design and optimize inductors and transformers for switched-mode power converters This course assumes prior completion of courses 1 and 2: Introduction to Power Electronics, and Converter Circuits.

This course can also be taken for academic credit as ECEA 5606, part of CU Boulder’s Master of Science in Electrical Engineering degree. Nanophotonics and Detectors Introduction This course dives into nanophotonic light emitting devices and optical detectors, including metal semiconductors, metal semiconductor insulators, and pn junctions. We will also cover photoconductors, avalanche photodiodes, and photomultiplier tubes. Weekly homework problem sets will challenge you to apply the principles of analysis and design we cover in preparation for real-world problems. Course Learning Outcomes At the end of this course you will be able to… (1) Use nanophotonic effects (low dimensional structures) to engineer lasers (2) Apply low dimensional structures to photonic device design (3) Select and design optical detector for given system and application

This course is the fourth course in the Electrodynamics series, and is directly proceeded by Electrodynamics: Electric and Magnetic Fields. Previously, we have learned about visualization of fields and solutions which were not time dependent. Here, we will return to Maxwell's Equations and use them to produce wave equations which can be used to analyze complex systems, such as oscillating dipoles. We will also introduce AC circuits, and how they can be simplified, solved, and applied. Learners will: • Have a complete understanding of Maxwell's Equations and how they relate to the magnetic and electric potentials. • Be able to solve problems related to moving charges, and add relativistic corrections to the equations • Understand the different components in AC circuits, and how their presence can change the function of the circuit. The approach taken in this course complements traditional approaches, covering a fairly complete treatment of the physics of electricity and magnetism, and adds Feynman’s unique and vital approach to grasping a picture of the physical universe. Furthermore, this course uniquely provides the link between the knowledge of electrodynamics and its practical applications to research in materials science, information technology, electrical engineering, chemistry, chemical engineering, energy storage, energy harvesting, and other materials related fields.

Welcome to Quantitative Formal Modeling and Worst-Case Performance Analysis. In this course, you will learn about modeling and solving performance problems in a fashion popular in theoretical computer science, and generally train your abstract thinking skills. After finishing this course, you have learned to think about the behavior of systems in terms of token production and consumption, and you are able to formalize this thinking mathematically in terms of prefix orders and counting functions. You have learned about Petri-nets, about timing, and about scheduling of token consumption/production systems, and for the special class of Petri-nets known as single-rate dataflow graphs, you will know how to perform a worst-case analysis of basic performance metrics, like throughput, latency and buffering. Disclaimer: As you will notice, there is an abundance of small examples in this course, but at first sight there are not many industrial size systems being discussed. The reason for this is two-fold. Firstly, it is not my intention to teach you performance analysis skills up to the level of what you will need in industry. Rather, I would like to teach you to think about modeling and performance analysis in general and abstract terms, because that is what you will need to do whenever you encounter any performance analysis problem in the future. After all, abstract thinking is the most revered skill required for any academic-level job in any engineering discipline, and if you are able to phrase your problems mathematically, it will become easier for you to spot mistakes, to communicate your ideas with others, and you have already made a big step towards actually solving the problem. Secondly, although dataflow techniques are applicable and being used in industry, the subclass of single-rate dataflow is too restrictive to be of practical use in large modeling examples. The analysis principles of other dataflow techniques, however, are all based on single-rate dataflow. So this course is a good primer for any more advanced course on the topic. This course is part of the university course on Quantitative Evaluation of Embedded Systems (QEES) as given in the Embedded Systems master curriculum of the EIT-Digital university, and of the Dutch 3TU consortium consisting of TU/e (Eindhoven), TUD (Delft) and UT (Twente). The course material is exactly the same as the first three weeks of QEES, but the examination of QEES is at a slightly higher level of difficulty, which cannot (yet) be obtained in an online course.

In this course, you will learn how to analyze map data using different data types and methods to answer geographic questions. First, you will learn how to filter a data set using different types of queries to find just the data you need to answer a particular question. Then, we will discuss simple yet powerful analysis methods that use vector data to find spatial relationships within and between data sets. In this section, you will also learn about how to use ModelBuilder, a simple but powerful tool for building analysis flowcharts that can then also be run as models. You will then learn how to find, understand, and use remotely sensed data such as satellite imagery, as a rich source of GIS data. You will then learn how to analyze raster data. Finally, you will complete your own project where you get to try out the new skills and tools you have learned about in this course. Note: software is not provided for this course.