Coursera. Motivation and Terminology of Automatic Process Control - Practical Process Control by Control Guru. Automatic control systems enable us to operate our processes in a safe and profitable manner. Consider, as on this site, processes with streams comprised of gases, liquids, powders, slurries and melts. Control systems achieve this "safe and profitable" objective by continually measuring process variables such as temperature, pressure, level, flow and concentration - and taking actions such as opening valves, slowing down pumps and turning up heaters - all so that the measured process variables are maintained at operator specified set point values. Safety First The overriding motivation for automatic control is safety, which encompasses the safety of people, the environment and equipment. The safety of plant personnel and people in the community are the highest priority in any plant operation.
The design of a process and associated control system must always make human safety the primary objective. Suppose we are making a film or sheet product. Copyright © 2006 by Douglas J. Sebrae. Academic Earth | Online Courses | Academic Video Lectures. Canal de UCLACourses. History 2D: Science, Magic, and Religion, Lecture 1, UCLA 118,378 views 5 years ago Lecture Title: "Course Introduction" March 31st, 2009 Professor Courtenay Raia lectures on science and religion as historical phenomena that have evolved over time. Examines the earlier mind-set before 1700 when into science fitted elements that came eventually to be seen as magical. THe course also question how Western cosmologies became "disenchanted. " Magical tradition transformed into modern mysticisms is also examined as well as the political implications of these movements.
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Open Courses for Free | Open Learning Initiative. At Harvard Extension School, free and open learning is hardly a new concept. In fact, the Extension School was founded with this mission in mind: to create an affordable way for any motivated student to take courses at Harvard. We stay true to this mission today, offering several free courses and nearly 800 for-credit courses at reasonable tuition rates.
Explore our series of free or low-cost courses below. In addition, you can also browse Harvard University's Digital Learning Portal, which features online learning content from across the University, both free and fee-based options. Video accessibility. If you are unable to easily access any of the videos below, you may submit a request for accommodation, and we will work with you on your request. Abstract Algebra In these free videotaped lectures, Professor Gross presents an array of algebraic concepts. The Ancient Greek Hero American Poetry from the Mayflower through Emerson Bits China Intensive Introduction to Computer Science Terms of Use. Cursos Gratuitos. A Fundação Getulio Vargas é a primeira instituição brasileira a ser membro do OpenCourseWare Consortium – OCWC –, um consórcio de instituições de ensino de diversos países que oferecem conteúdos e materiais didáticos sem custo, pela internet.
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Em caso de dúvidas, consulte a página sobre Dúvidas Frequentes. Cursos. OCW Consortium. Open Education. OCW: Home. Free Online Course Materials | MIT OpenCourseWare. Learning From Data - Online Course. A real Caltech course, not a watered-down version on YouTube & iTunes Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications.
ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. What is learning? Live Lectures This course was broadcast live from the lecture hall at Caltech in April and May 2012. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible? The Linear Model I - Linear classification and linear regression. Error and Noise - The principled choice of error measures.