Acerca de este curso: 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
Para quién es esta clase: This course is primarily aimed at students and working engineers focused on embedded systems for the Industrial Internet of Things (IIoT).
Creada por: University of Colorado Boulder
Enseñado por: David Sluiter, Professor Adjunct
Electrical, Computer, and Energy Engineering
| Nivel | Intermedio |
| Idioma | Inglés (English) |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
Programa
SEMANA 1
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 videos, 1 lectura
- Vídeo: Segment 1 - Learning Outcomes, Introduction to a Design Process
- Vídeo: Segment 2 - Requirements, Scope, Schedule, Resources, Heap Chart
- Vídeo: Segment 3 - Roles and Responsibilities
- Vídeo: Segment 4 - Process: Architecture Definition, Design Planning
- Vídeo: Segment 5 - Process: Architecture Definition, Design Planning 2
- Vídeo: Segment 6 - Process: Develop
- Vídeo: Segment 7 - Process: Verification
- Vídeo: Segment 8 - Process: Manufacture
- Vídeo: Segment 9 - Process: Deploy
- Vídeo: Segment 10 - Process: Validation
- Vídeo: Segment 11 - Temperature
- Reading: Project Planning and Machine Learning Course Slides
Calificado: Quiz 1
Calificado: Product tear down, build a bill of materials (BOM)
SEMANA 2
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
- Vídeo: Segment 1 - Learning Outcomes, Introduction to Thermistors
- Vídeo: Segment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance
- Vídeo: Segment 3 - Basic Sensor Circuit
- Vídeo: Segment 4 - Accuracy Example
- Vídeo: Segment 5 - Calculating Rtherm
- Vídeo: Segment 6 - Validating Calibration
- Vídeo: Segment 7 - Filtering Techniques
- Vídeo: Segment 8 - Block, Object and Key-Value Storage Devices
- Vídeo: Segment 9 - Filesystem Basics
- Vídeo: Segment 10 - A File on a Hard Drive
- Vídeo: Segment 11 - A File on a Solid State Drive
- Vídeo: Segment 12 - File System: NFS
- Vídeo: Segment 13 - How Big is "Big"?
- Vídeo: Segment 14 - Traditional File System Bottlenecks
- Vídeo: Segment 15 - Parallel Distributed File Systems: Hadoop, Lustre
Calificado: Quiz 2
SEMANA 3
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 videos, 1 lectura
- Vídeo: Segment 1 - Learning Outcomes
- Vídeo: Segment 2 - AI Backgrounder
- Vídeo: Segment 3 - Machine Learning, What is it?
- Vídeo: Segment 4 - Machine Learning Schools of Thought
- Vídeo: Segment 5 - Get the Tools
- Vídeo: Segment 6 - Categories of Machine Learning
- Vídeo: Segment 7 - Supervised Learning, Linear Regression 1
- Vídeo: Segment 8 - Supervised Leraning, Linear Regression 2
- Vídeo: Segment 9 - Supervised Learning, Linear Regression 3
- Vídeo: Segment 10 - Supervised Learning, Linear Regression 4
- Vídeo: Segment 11 - Supervised Learning, Bayes Theorem
- Vídeo: Segment 12 - Supervised Learning, Naive Bayes
- Vídeo: Segment 13 - Supervised Learning, Support Vector Machines (SVM) Introduction
- Vídeo: Segment 14 - Supervised Learning, SVMs
- Vídeo: Segment 15 - Unsupervised Learning, K-Means
- Vídeo: Segment 16 - Reinforcement Learning
- Vídeo: Segment 17 - Supervised Learning, Deep Learning
- Vídeo: Segment 18 - Rick Rashid, Natural Language Processing
- Vídeo: Segment 19 - Deep Learning, Hearing Aid
- Vídeo: Segment 20 - Machine Learning in IIoT
- Vídeo: Segment 21 - Machine Learning Summary
- Reading: Source code examples and magazine articles
Calificado: Quiz 3
SEMANA 4
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, 1 lectura
- Vídeo: Segment 1 - Learning Outcomes, Definition of Big Data
- Vídeo: Segment 2 - Importance of Big Data, Characteristics of Big Data
- Vídeo: Segment 3 - Size of Big Data
- Vídeo: Segment 4 - Introduction to Predictive Analytics
- Vídeo: Segment 5 - Role of Statistics and Data Mining
- Vídeo: Segment 6 - Machine Learning, Generalization and Discrimination
- Vídeo: Segment 7 - Frameworks, Testing and Validating
- Vídeo: Segment 8 - Bias and Variance in your Data
- Vídeo: Segment 9 - Out-of-sample Data and Learning Curves
- Vídeo: Segment 10 - Cross Validation
- Vídeo: Segment 11 - Model Complexity, Over- and Under-fitting
- Vídeo: Segment 12 - Processing Your Data Prior to Machine Learning
- Vídeo: Segment 13 - Good Data, Smart Data
- Vídeo: Segment 14 - Visualizing Your Data
- Vídeo: Segment 15 - Principal Component Analysis (PCA)
- Vídeo: Segment 16 - Prognostic Health Management, Hadoop Machine Learning Library
- Vídeo: Segment 17 - My Example: Predicting NFL Football Winners
- Vídeo: Segment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data
- Reading: Source code example
Calificado: Quiz 4
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Creadores
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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
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