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
Ce cours fait partie de la Spécialisation Developing Industrial Internet of Things
Project Planning and Machine Learning
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À propos de ce cours
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Ce que vous allez apprendre
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.
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
111 heures pour terminer
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 vidéos (Total 112 min), 2 lectures, 2 quiz
12 vidéos
Introduction1 min
Segment 1 - Learning Outcomes, Introduction to a Design Process12 min
Segment 2 - Requirements, Scope, Schedule, Resources, Heap Chart15 min
Segment 3 - Roles and Responsibilities6 min
Segment 4 - Process: Architecture Definition, Design Planning13 min
Segment 5 - Process: Architecture Definition, Design Planning 218 min
Segment 6 - Process: Develop9 min
Segment 7 - Process: Verification11 min
Segment 8 - Process: Manufacture2 min
Segment 9 - Process: Deploy10 min
Segment 10 - Process: Validation6 min
Segment 11 - Temperature5 min
2 lectures
A Note from the Instructor5 min
Project Planning and Machine Learning Course Slides10 min
1 exercice pour s'entraîner
Quiz 130 min
Semaine
22 heures pour terminer
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 vidéos (Total 103 min), 1 quiz
16 vidéos
Introduction28s
Segment 1 - Learning Outcomes, Introduction to Thermistors3 min
Segment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance6 min
Segment 3 - Basic Sensor Circuit5 min
Segment 4 - Accuracy Example2 min
Segment 5 - Calculating Rtherm2 min
Segment 6 - Validating Calibration5 min
Segment 7 - Filtering Techniques11 min
Segment 8 - Block, Object and Key-Value Storage Devices15 min
Segment 9 - Filesystem Basics3 min
Segment 10 - A File on a Hard Drive5 min
Segment 11 - A File on a Solid State Drive8 min
Segment 12 - File System: NFS4 min
Segment 13 - How Big is "Big"?8 min
Segment 14 - Traditional File System Bottlenecks3 min
Segment 15 - Parallel Distributed File Systems: Hadoop, Lustre13 min
1 exercice pour s'entraîner
Quiz 230 min
Semaine
33 heures pour terminer
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 vidéos (Total 132 min), 1 lecture, 1 quiz
22 vidéos
Introduction30s
Segment 1 - Learning Outcomes1 min
Segment 2 - AI Backgrounder6 min
Segment 3 - Machine Learning, What is it?6 min
Segment 4 - Machine Learning Schools of Thought9 min
Segment 5 - Get the Tools3 min
Segment 6 - Categories of Machine Learning5 min
Segment 7 - Supervised Learning, Linear Regression 17 min
Segment 8 - Supervised Learning, Linear Regression 29 min
Segment 9 - Supervised Learning, Linear Regression 38 min
Segment 10 - Supervised Learning, Linear Regression 49 min
Segment 11 - Supervised Learning, Bayes Theorem4 min
Segment 12 - Supervised Learning, Naive Bayes9 min
Segment 13 - Supervised Learning, Support Vector Machines (SVM) Introduction55s
Segment 14 - Supervised Learning, SVMs12 min
Segment 15 - Unsupervised Learning, K-Means11 min
Segment 16 - Reinforcement Learning46s
Segment 17 - Supervised Learning, Deep Learning2 min
Segment 18 - Rick Rashid, Natural Language Processing8 min
Segment 19 - Deep Learning, Hearing Aid2 min
Segment 20 - Machine Learning in IIoT4 min
Segment 21 - Machine Learning Summary4 min
1 lecture
Source code examples and magazine articles10 min
1 exercice pour s'entraîner
Quiz 330 min
Semaine
43 heures pour terminer
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 vidéos (Total 119 min), 1 lecture, 1 quiz
19 vidéos
Introduction33s
Segment 1 - Learning Outcomes, Definition of Big Data3 min
Segment 2 - Importance of Big Data, Characteristics of Big Data4 min
Segment 3 - Size of Big Data4 min
Segment 4 - Introduction to Predictive Analytics2 min
Segment 5 - Role of Statistics and Data Mining3 min
Segment 6 - Machine Learning, Generalization and Discrimination7 min
Segment 7 - Frameworks, Testing and Validating5 min
Segment 8 - Bias and Variance in your Data3 min
Segment 9 - Out-of-sample Data and Learning Curves5 min
Segment 10 - Cross Validation5 min
Segment 11 - Model Complexity, Over- and Under-fitting3 min
Segment 12 - Processing Your Data Prior to Machine Learning8 min
Segment 13 - Good Data, Smart Data6 min
Segment 14 - Visualizing Your Data1 min
Segment 15 - Principal Component Analysis (PCA)2 min
Segment 16 - Prognostic Health Management, Hadoop Machine Learning Library11 min
Segment 17 - My Example: Predicting NFL Football Winners18 min
Segment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data20 min
1 lecture
Source code example10 min
1 exercice pour s'entraîner
Quiz 430 min
Enseignant
David Sluiter
Professor AdjunctElectrical, Computer, and Energy Engineering
Commencez à travailler pour obtenir votre master
Ce cours fait partie du diplôme intégralement en ligne Master of Science in Electrical Engineering de Université du Colorado à Boulder.
Si vous êtes admis au programme complet, vos cours sont pris en compte dans votre apprentissage diplômant.
À propos de Université du Colorado à 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.
À propos du Spécialisation 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.
Foire Aux Questions
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