Acerca de este Curso

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Certificado para compartir
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100 % en línea
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Fechas límite flexibles
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Nivel intermedio
Aprox. 17 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 17 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Instructor

ofrecido por

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Universidad de Colorado en Boulder

Comienza a trabajar para obtener tu maestría

Este curso es parte del Master of Science in Electrical Engineering completamente en línea de Universidad de Colorado en Boulder. Si eres aceptado en el programa completo, tus cursos cuentan para tu título.

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

10 horas para completar

Project Planning and Staffing

10 horas para completar
12 videos (Total 112 minutos), 2 lecturas, 2 cuestionarios
12 videos
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
2

Semana 2

2 horas para completar

Sensors and File Systems

2 horas para completar
16 videos (Total 103 minutos)
16 videos
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
3

Semana 3

3 horas para completar

Machine Learning

3 horas para completar
22 videos (Total 132 minutos)
22 videos
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
4

Semana 4

2 horas para completar

Big Data Analytics

2 horas para completar
19 videos (Total 119 minutos)
19 videos
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

Principales reseñas sobre PROJECT PLANNING AND MACHINE LEARNING

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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

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