This course can also be taken for academic credit as ECEA 5386, part of CU Boulder’s Master of Science in Electrical Engineering degree.
Developing Industrial Internet of Things 특화 과정콜로라도 대학교 볼더 캠퍼스이 강좌에 대하여
최근 조회 13,661회
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 17시간 필요
영어
자막: 영어
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 2번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 17시간 필요
영어
자막: 영어
제공자:
콜로라도 대학교 볼더 캠퍼스
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.
석사 학위 취득 시작
This 강좌 is part of the 100% online Master of Science in Electrical Engineering from 콜로라도 대학교 볼더 캠퍼스.
If you are admitted to the full program, your courses count towards your degree learning.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 10시간 필요
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.
완료하는 데 10시간 필요
12개 동영상 (총 112분), 2 개의 읽기 자료, 2 개의 테스트
12개의 동영상
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
2개의 읽기 자료
Access to Course Resources10m
A Note from the Instructor5m
1개 연습문제
Module 1 Quiz10m
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
16개 동영상 (총 103분)
16개의 동영상
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
1개 연습문제
Module 2 Quiz18m
완료하는 데 3시간 필요
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.
완료하는 데 3시간 필요
22개 동영상 (총 132분)
22개의 동영상
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) Introduction55
Segment 14 - Supervised Learning, SVMs12m
Segment 15 - Unsupervised Learning, K-Means11m
Segment 16 - Reinforcement Learning46
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
1개 연습문제
Module 3 Quiz22m
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
19개 동영상 (총 119분)
19개의 동영상
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
1개 연습문제
Module 4 Quiz26m
검토
PROJECT PLANNING AND MACHINE LEARNING의 최상위 리뷰
DB 제공2020년 7월 6일
Very well designed and delivered Course. Thank you for the opportunity. Special Thanks to Prof Dave Sluiter
AN 제공2020년 6월 2일
It was a good course to get a background and basic clearing while developing IIoT projects...
RK 제공2020년 5월 15일
Got good idea about predictive analysis and ML concepts used in IoT design
AB 제공2019년 4월 1일
quizzes can be tougher. great course!\n\n100% history oriented.
Developing Industrial Internet of Things 특화 과정 정보
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.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강의 및 과제 이용 권한은 등록 유형에 따라 다릅니다. 청강 모드로 강좌를 수강하면 대부분의 강좌 자료를 무료로 볼 수 있습니다. 채점된 과제를 이용하고 수료증을 받으려면 청강 도중 또는 이후에 수료증 경험을 구매해야 합니다. 청강 옵션이 표시되지 않는 경우:
- 강좌에서 청강 옵션을 제공하지 않을 수 있습니다. 대신 무료 평가판을 사용하거나 재정 지원을 신청할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
Is financial aid available?
예, Coursera에서는 수업료를 낼 수 없는 학습자를 위해 재정 지원을 제공합니다. 왼쪽에 있는 등록 버튼 아래 재정 지원 링크를 클릭하면 지원할 수 있습니다. 신청서를 작성하라는 메시지가 표시되며 승인되면 알림을 받습니다. 성취 프로젝트를 포함하여 전문 분야의 각 강좌에서 이 단계를 완료해야 합니다. 자세히 알아보기.
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
