Este curso forma parte de Programa especializado: Developing Industrial Internet of Things

4.7
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92 calificaciones

David Sluiter

6363 ya inscrito

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Puedes obtener créditos de la Universidad de Colorado en Boulder que cuentan para la Master of Science in Electrical Engineering

Programa especializado: Developing Industrial Internet of ThingsUniversidad de Colorado en Boulder

Acerca de este Curso

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This course can also be taken for academic credit as ECEA 5386, part of CU Boulder’s Master of Science in Electrical Engineering degree.
This is part 2 of the specialization. In this course students will learn :
  * How to staff, plan and execute a project
  * How to build a bill of materials for a product
  * How to calibrate sensors and validate sensor measurements
  * How hard drives and solid state drives operate
  * How basic file systems operate, and types of file systems used to store big data
  * How machine learning algorithms work - a basic introduction
  * Why we want to study big data and how to prepare data for machine learning algorithms

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Curso 2 de 3 en

Programa especializado: Developing Industrial Internet of Things

Nivel intermedio

Aprox. 17 horas para completar

Inglés (English)

Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)

Qué aprenderás

How to staff, plan and execute a project.
How to build a bill of materials for a product.
How to calibrate sensors and validate sensor measurements.
How hard drives and solid state drives operate.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 2 de 3 en

Programa especializado: Developing Industrial Internet of Things

Nivel intermedio

Aprox. 17 horas para completar

Inglés (English)

Subtítulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish)

Instructor

Calificación del instructor

4,71/5 (23 calificaciones)

David Sluiter

Professor Adjunct

Electrical, Computer, and Energy Engineering

26.812 alumnos

4 cursos

ofrecido por

Universidad de Colorado en Boulder

CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

Comienza a trabajar para obtener tu título

Este curso forma parte de un programa de grado ofrecido por Universidad de Colorado en Boulder. Si te inscribes en un curso sin título de grado a través de la universidad y lo completas en línea, cuenta como horas de crédito para un programa de grado en CU-Boulder. Todo lo que tienes que hacer es solicitarlo a través de la universidad.

Master of Science in Electrical Engineering

Universidad de Colorado en Boulder

Inscríbete para obtener créditos

Programa - Qué aprenderás en este curso

Semana

Semana 1

10 horas para completar

Project Planning and Staffing

In this module I share with you my experience in product planning, staffing and execution. You will perform a product tear down, write a paper about your tear down and build a bill of materials (BOM) for that product.

10 horas para completar

12 videos (Total 112 minutos), 2 lecturas, 2 cuestionarios

12 videos

Introduction1m

Segment 1 - Learning Outcomes, Introduction to a Design Process12m

Segment 2 - Requirements, Scope, Schedule, Resources, Heap Chart15m

Segment 3 - Roles and Responsibilities6m

Segment 4 - Process: Architecture Definition, Design Planning13m

Segment 5 - Process: Architecture Definition, Design Planning 218m

Segment 6 - Process: Develop9m

Segment 7 - Process: Verification11m

Segment 8 - Process: Manufacture2m

Segment 9 - Process: Deploy10m

Segment 10 - Process: Validation6m

Segment 11 - Temperature5m

2 lecturas

Access to Course Resources10m

A Note from the Instructor5m

1 ejercicio de práctica

Module 1 Quiz10m

Semana

Semana 2

2 horas para completar

Sensors and File Systems

In this module you will learn about sensors, and in this case, a temperature sensor. You will learn how to calibrate and then validate that a temperature sensor is producing accurate results. We will study how data is stored on hard drives and solid state drives. We will take a brief look at file systems used to store large data sets.

2 horas para completar

16 videos (Total 103 minutos)

16 videos

Introduction28s

Segment 1 - Learning Outcomes, Introduction to Thermistors3m

Segment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance6m

Segment 3 - Basic Sensor Circuit5m

Segment 4 - Accuracy Example2m

Segment 5 - Calculating Rtherm2m

Segment 6 - Validating Calibration5m

Segment 7 - Filtering Techniques11m

Segment 8 - Block, Object and Key-Value Storage Devices15m

Segment 9 - Filesystem Basics3m

Segment 10 - A File on a Hard Drive5m

Segment 11 - A File on a Solid State Drive8m

Segment 12 - File System: NFS4m

Segment 13 - How Big is "Big"?8m

Segment 14 - Traditional File System Bottlenecks3m

Segment 15 - Parallel Distributed File Systems: Hadoop, Lustre13m

1 ejercicio de práctica

Module 2 Quiz18m

Semana

Semana 3

3 horas para completar

Machine Learning

In this module we look at machine learning (ML), what it is and how it works. We take a look at a couple supervised learning algorithms and 1 unsupervised learning algorithm. No coding is required of you. Instead I provide working source code to you so you can play around with these algorithms. I wrap up by providing some examples of how ML can be used in the IIoT space.

3 horas para completar

22 videos (Total 132 minutos)

22 videos

Introduction30s

Segment 1 - Learning Outcomes1m

Segment 2 - AI Backgrounder6m

Segment 3 - Machine Learning, What is it?6m

Segment 4 - Machine Learning Schools of Thought9m

Segment 5 - Get the Tools3m

Segment 6 - Categories of Machine Learning5m

Segment 7 - Supervised Learning, Linear Regression 17m

Segment 8 - Supervised Learning, Linear Regression 29m

Segment 9 - Supervised Learning, Linear Regression 38m

Segment 10 - Supervised Learning, Linear Regression 49m

Segment 11 - Supervised Learning, Bayes Theorem4m

Segment 12 - Supervised Learning, Naive Bayes9m

Segment 13 - Supervised Learning, Support Vector Machines (SVM) Introduction55s

Segment 14 - Supervised Learning, SVMs12m

Segment 15 - Unsupervised Learning, K-Means11m

Segment 16 - Reinforcement Learning46s

Segment 17 - Supervised Learning, Deep Learning2m

Segment 18 - Rick Rashid, Natural Language Processing8m

Segment 19 - Deep Learning, Hearing Aid2m

Segment 20 - Machine Learning in IIoT4m

Segment 21 - Machine Learning Summary4m

1 ejercicio de práctica

Module 3 Quiz22m

Semana

Semana 4

2 horas para completar

Big Data Analytics

In this module you will learn about big data and why we want to study it. You will learn about issues that can arise with a data set and the importance of properly preparing data prior to a ML exercise.

2 horas para completar

19 videos (Total 119 minutos)

19 videos

Introduction33s

Segment 1 - Learning Outcomes, Definition of Big Data3m

Segment 2 - Importance of Big Data, Characteristics of Big Data4m

Segment 3 - Size of Big Data4m

Segment 4 - Introduction to Predictive Analytics2m

Segment 5 - Role of Statistics and Data Mining3m

Segment 6 - Machine Learning, Generalization and Discrimination7m

Segment 7 - Frameworks, Testing and Validating5m

Segment 8 - Bias and Variance in your Data3m

Segment 9 - Out-of-sample Data and Learning Curves5m

Segment 10 - Cross Validation5m

Segment 11 - Model Complexity, Over- and Under-fitting3m

Segment 12 - Processing Your Data Prior to Machine Learning8m

Segment 13 - Good Data, Smart Data6m

Segment 14 - Visualizing Your Data1m

Segment 15 - Principal Component Analysis (PCA)2m

Segment 16 - Prognostic Health Management, Hadoop Machine Learning Library11m

Segment 17 - My Example: Predicting NFL Football Winners18m

Segment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data20m

1 ejercicio de práctica

Module 4 Quiz26m

Reseñas

4.7

21 reseña

5 stars
77,41 %
4 stars
15,05 %
3 stars
4,30 %
2 stars
2,15 %
1 star
1,07 %

Principales reseñas sobre PROJECT PLANNING AND MACHINE LEARNING

por RK15 de may. de 2020

Got good idea about predictive analysis and ML concepts used in IoT design

por M26 de feb. de 2022

Brilliant course! A must for all the budding IoT professionals. Thanks Prof David, The University of Colorado and Coursera.

por JS28 de may. de 2022

Awsomw Materials and Explanations. Dedicatedly you ve to preparare if you wants to improvizing yourself. Thanks by Sam.

por AB1 de abr. de 2019

quizzes can be tougher.

great course!

100% history oriented.

Ver todas las reseñas

Acerca de Programa especializado: Developing Industrial Internet of Things

The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here.

In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment. You'll encounter market sizes and opportunities, operating systems, networking concepts, many security topics, how to plan, staff and execute a project plan, sensors, file systems and how storage devices work, machine learning and big data analytics, an introduction to SystemC, techniques for debugging deeply embedded systems, promoting technical ideas within a company and learning from failures. In addition, students will learn several key business concepts important for engineers to understand, like CapEx (capital expenditure) for buying a piece of lab equipment and OpEx (operational expense) for rent, utilities and employee salaries.

Developing Industrial Internet of Things

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Project Planning and Machine Learning

Acerca de este Curso

Qué aprenderás