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
Project Planning and Machine Learning
от партнера
Project Planning and Machine Learning
University of Colorado Boulder
Программа курса: что вы изучите
Неделя
1Project 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 видео (всего 112 мин.), 1 материал для самостоятельного изучения, 2 тестов
12 видео
Introduction1мин
Segment 1 - Learning Outcomes, Introduction to a Design Process12мин
Segment 2 - Requirements, Scope, Schedule, Resources, Heap Chart15мин
Segment 3 - Roles and Responsibilities6мин
Segment 4 - Process: Architecture Definition, Design Planning13мин
Segment 5 - Process: Architecture Definition, Design Planning 218мин
Segment 6 - Process: Develop9мин
Segment 7 - Process: Verification11мин
Segment 8 - Process: Manufacture2мин
Segment 9 - Process: Deploy10мин
Segment 10 - Process: Validation6мин
Segment 11 - Temperature5мин
1 материал для самостоятельного изучения
Project Planning and Machine Learning Course Slides10мин
1 практическое упражнение
Quiz 110мин
Неделя
2Sensors 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 видео (всего 103 мин.), 1 тест
16 видео
Introductionмин
Segment 1 - Learning Outcomes, Introduction to Thermistors3мин
Segment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance6мин
Segment 3 - Basic Sensor Circuit5мин
Segment 4 - Accuracy Example2мин
Segment 5 - Calculating Rtherm2мин
Segment 6 - Validating Calibration5мин
Segment 7 - Filtering Techniques11мин
Segment 8 - Block, Object and Key-Value Storage Devices15мин
Segment 9 - Filesystem Basics3мин
Segment 10 - A File on a Hard Drive5мин
Segment 11 - A File on a Solid State Drive8мин
Segment 12 - File System: NFS4мин
Segment 13 - How Big is "Big"?8мин
Segment 14 - Traditional File System Bottlenecks3мин
Segment 15 - Parallel Distributed File Systems: Hadoop, Lustre13мин
1 практическое упражнение
Quiz 218мин
Неделя
3Machine 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 видео (всего 132 мин.), 1 материал для самостоятельного изучения, 1 тест
22 видео
Introductionмин
Segment 1 - Learning Outcomes1мин
Segment 2 - AI Backgrounder6мин
Segment 3 - Machine Learning, What is it?6мин
Segment 4 - Machine Learning Schools of Thought9мин
Segment 5 - Get the Tools3мин
Segment 6 - Categories of Machine Learning5мин
Segment 7 - Supervised Learning, Linear Regression 17мин
Segment 8 - Supervised Leraning, Linear Regression 29мин
Segment 9 - Supervised Learning, Linear Regression 38мин
Segment 10 - Supervised Learning, Linear Regression 49мин
Segment 11 - Supervised Learning, Bayes Theorem4мин
Segment 12 - Supervised Learning, Naive Bayes9мин
Segment 13 - Supervised Learning, Support Vector Machines (SVM) Introductionмин
Segment 14 - Supervised Learning, SVMs12мин
Segment 15 - Unsupervised Learning, K-Means11мин
Segment 16 - Reinforcement Learningмин
Segment 17 - Supervised Learning, Deep Learning2мин
Segment 18 - Rick Rashid, Natural Language Processing8мин
Segment 19 - Deep Learning, Hearing Aid2мин
Segment 20 - Machine Learning in IIoT4мин
Segment 21 - Machine Learning Summary4мин
1 материал для самостоятельного изучения
Source code examples and magazine articles10мин
1 практическое упражнение
Quiz 322мин
Неделя
4Big 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 видео (всего 119 мин.), 1 материал для самостоятельного изучения, 1 тест
19 видео
Introductionмин
Segment 1 - Learning Outcomes, Definition of Big Data3мин
Segment 2 - Importance of Big Data, Characteristics of Big Data4мин
Segment 3 - Size of Big Data4мин
Segment 4 - Introduction to Predictive Analytics2мин
Segment 5 - Role of Statistics and Data Mining3мин
Segment 6 - Machine Learning, Generalization and Discrimination7мин
Segment 7 - Frameworks, Testing and Validating5мин
Segment 8 - Bias and Variance in your Data3мин
Segment 9 - Out-of-sample Data and Learning Curves5мин
Segment 10 - Cross Validation5мин
Segment 11 - Model Complexity, Over- and Under-fitting3мин
Segment 12 - Processing Your Data Prior to Machine Learning8мин
Segment 13 - Good Data, Smart Data6мин
Segment 14 - Visualizing Your Data1мин
Segment 15 - Principal Component Analysis (PCA)2мин
Segment 16 - Prognostic Health Management, Hadoop Machine Learning Library11мин
Segment 17 - My Example: Predicting NFL Football Winners18мин
Segment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data20мин
1 материал для самостоятельного изучения
Source code example10мин
1 практическое упражнение
Quiz 426мин
Преподаватель
David Sluiter
Professor AdjunctElectrical, Computer, and Energy Engineering
О 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.
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