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
Este curso faz parte do Programa de cursos integrados Developing Industrial Internet of Things
Project Planning and Machine Learning
4.0
(6 classificações)
533 já inscritos!
oferecido por
Programa de cursos integrados Developing Industrial Internet of ThingsUniversidade do Colorado em Boulder
Programa - O que você aprenderá com este curso
Semana
110 horas para concluir
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 and build a bill of materials (BOM) for that product.
12 vídeos (total de (Total 112 mín.) min), 2 leituras, 2 testes
12 videos
Introduction1min
Segment 1 - Learning Outcomes, Introduction to a Design Process12min
Segment 2 - Requirements, Scope, Schedule, Resources, Heap Chart15min
Segment 3 - Roles and Responsibilities6min
Segment 4 - Process: Architecture Definition, Design Planning13min
Segment 5 - Process: Architecture Definition, Design Planning 218min
Segment 6 - Process: Develop9min
Segment 7 - Process: Verification11min
Segment 8 - Process: Manufacture2min
Segment 9 - Process: Deploy10min
Segment 10 - Process: Validation6min
Segment 11 - Temperature5min
2 leituras
A Note from the Instructor5min
Project Planning and Machine Learning Course Slides10min
1 exercício prático
Quiz 110min
Semana
22 horas para concluir
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.
16 vídeos (total de (Total 103 mín.) min), 1 teste
16 videos
Introduction28s
Segment 1 - Learning Outcomes, Introduction to Thermistors3min
Segment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance6min
Segment 3 - Basic Sensor Circuit5min
Segment 4 - Accuracy Example2min
Segment 5 - Calculating Rtherm2min
Segment 6 - Validating Calibration5min
Segment 7 - Filtering Techniques11min
Segment 8 - Block, Object and Key-Value Storage Devices15min
Segment 9 - Filesystem Basics3min
Segment 10 - A File on a Hard Drive5min
Segment 11 - A File on a Solid State Drive8min
Segment 12 - File System: NFS4min
Segment 13 - How Big is "Big"?8min
Segment 14 - Traditional File System Bottlenecks3min
Segment 15 - Parallel Distributed File Systems: Hadoop, Lustre13min
1 exercício prático
Quiz 218min
Semana
33 horas para concluir
Machine Learning
In this module we look at machine learning, 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.
22 vídeos (total de (Total 132 mín.) min), 1 leitura, 1 teste
22 videos
Introduction30s
Segment 1 - Learning Outcomes1min
Segment 2 - AI Backgrounder6min
Segment 3 - Machine Learning, What is it?6min
Segment 4 - Machine Learning Schools of Thought9min
Segment 5 - Get the Tools3min
Segment 6 - Categories of Machine Learning5min
Segment 7 - Supervised Learning, Linear Regression 17min
Segment 8 - Supervised Leraning, Linear Regression 29min
Segment 9 - Supervised Learning, Linear Regression 38min
Segment 10 - Supervised Learning, Linear Regression 49min
Segment 11 - Supervised Learning, Bayes Theorem4min
Segment 12 - Supervised Learning, Naive Bayes9min
Segment 13 - Supervised Learning, Support Vector Machines (SVM) Introduction55s
Segment 14 - Supervised Learning, SVMs12min
Segment 15 - Unsupervised Learning, K-Means11min
Segment 16 - Reinforcement Learning46s
Segment 17 - Supervised Learning, Deep Learning2min
Segment 18 - Rick Rashid, Natural Language Processing8min
Segment 19 - Deep Learning, Hearing Aid2min
Segment 20 - Machine Learning in IIoT4min
Segment 21 - Machine Learning Summary4min
1 leituras
Source code examples and magazine articles10min
1 exercício prático
Quiz 322min
Semana
43 horas para concluir
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.
19 vídeos (total de (Total 119 mín.) min), 1 leitura, 1 teste
19 videos
Introduction33s
Segment 1 - Learning Outcomes, Definition of Big Data3min
Segment 2 - Importance of Big Data, Characteristics of Big Data4min
Segment 3 - Size of Big Data4min
Segment 4 - Introduction to Predictive Analytics2min
Segment 5 - Role of Statistics and Data Mining3min
Segment 6 - Machine Learning, Generalization and Discrimination7min
Segment 7 - Frameworks, Testing and Validating5min
Segment 8 - Bias and Variance in your Data3min
Segment 9 - Out-of-sample Data and Learning Curves5min
Segment 10 - Cross Validation5min
Segment 11 - Model Complexity, Over- and Under-fitting3min
Segment 12 - Processing Your Data Prior to Machine Learning8min
Segment 13 - Good Data, Smart Data6min
Segment 14 - Visualizing Your Data1min
Segment 15 - Principal Component Analysis (PCA)2min
Segment 16 - Prognostic Health Management, Hadoop Machine Learning Library11min
Segment 17 - My Example: Predicting NFL Football Winners18min
Segment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data20min
1 leituras
Source code example10min
1 exercício prático
Quiz 426min
Instrutores
David Sluiter
Professor AdjunctElectrical, Computer, and Energy Engineering
Sobre Universidade do Colorado em 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.
Sobre o Programa de cursos integrados Developing Industrial Internet of Things
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.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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