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
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Developing Industrial Internet of Things
Project Planning and Machine Learning
von
Über diesen Kurs
5,668 kürzliche Aufrufe
Was Sie lernen werden
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.
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 3 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Stufe „Mittel“
Ca. 14 Stunden zum Abschließen
Empfohlen: 6 hours/week
Englisch
Untertitel: Englisch
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
110 Stunden zum Abschließen
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.
12 Videos (Gesamt 112 min), 2 Lektüren, 2 Quiz
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 Lektüren
Access to Course Resources10m
A Note from the Instructor5m
1 praktische Übung
Module 1 Quiz10m
Woche
22 Stunden zum Abschließen
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 Videos (Gesamt 103 min), 1 Quiz
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 praktische Übung
Module 2 Quiz18m
Woche
33 Stunden zum Abschließen
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.
22 Videos (Gesamt 132 min), 1 Quiz
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) 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
1 praktische Übung
Module 3 Quiz22m
Woche
42 Stunden zum Abschließen
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 Videos (Gesamt 119 min), 1 Quiz
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 praktische Übung
Module 4 Quiz26m
Dozent
David Sluiter
Professor AdjunctElectrical, Computer, and Energy Engineering
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Dieses Kurs ist Teil des reinen Onlineabschlusses Master of Science in Electrical Engineering von University of Colorado Boulder.
Wenn Sie in das komplette Programm aufgenommen werden, werden Ihre Kurse auf Ihren Abschluss angerechnet.
Über University of Colorado 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.
Über den Spezialisierung 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.
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