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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/industrial-iot-project-planning-machine-learning
课程评论：没有评论
课程名称：项目规划与机器学习 课程概述：该课程可作为学分课程修读，属于科罗拉多大学博尔德分校的电气工程硕士学位（ECEA 5386）。这是该专业化的第二部分。在本课程中，学生将学习： - 如何进行项目的人力资源配置、规划和执行 - 如何为产品构建物料清单（BOM） - 如何校准传感器并验证传感器的测量值 - 硬盘和固态硬盘的工作原理 - 基本文件系统的操作及其在大数据存储中的应用 - 机器学习算法的基本原理 - 为什么我们要研究大数据以及如何为机器学习算法准备数据 课程大纲： 第1部分：项目规划与人员配置 描述：在这一模块中，讲师将分享产品规划、人员配置和执行的经验。学生将进行产品拆解，撰写有关拆解的论文，并为该产品构建物料清单（BOM）。 第2部分：传感器与文件系统 描述：本模块将学习传感器，特别是温度传感器。学生将学习如何校准并验证温度传感器的准确性。我们将研究数据在硬盘和固态硬盘上的存储方式，并简要了解用于存储大型数据集的文件系统。 第3部分：机器学习 描述：本模块将探讨机器学习（ML）的概念及其工作原理。我们将详细查看几个监督学习算法和一个无监督学习算法。课程不要求编写代码，讲师会提供可供试用的工作源代码。此外，还将展示机器学习在工业物联网（IIoT）领域的应用示例。 第4部分：大数据分析 描述：本模块将介绍大数据及其研究的重要性。学生将学习数据集中可能出现的问题以及在进行机器学习前适当准备数据的重要性。 通过本课程，学生将掌握项目规划、传感器校准、文件系统基础、机器学习概念及大数据分析的关键技能，为其未来的职业发展打下坚实基础。
Part: 1
Title:Project Planning and Staffing
Description: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.
Part: 2
Title:Sensors and File Systems
Description: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.
Part: 3
Title:Machine Learning
Description: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.
Part: 4
Title:Big Data Analytics
Description: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.
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
