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Project Planning and Machine Learning
  • Level Professional
  • المدة 18 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

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

الوحدات

Course Introduction

    • 2 Readings
    • 1 Discussion
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1 Discussions

  • Introduction Discussion Forum

2 Readings

  • Access to Course Resources
  • A Note from the Instructor

Lectures

    • 12 Videos
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12 Videos

  • Introduction
  • Segment 1 - Learning Outcomes, Introduction to a Design Process
  • Segment 2 - Requirements, Scope, Schedule, Resources, Heap Chart
  • Segment 3 - Roles and Responsibilities
  • Segment 4 - Process: Architecture Definition, Design Planning
  • Segment 5 - Process: Architecture Definition, Design Planning 2
  • Segment 6 - Process: Develop
  • Segment 7 - Process: Verification
  • Segment 8 - Process: Manufacture
  • Segment 9 - Process: Deploy
  • Segment 10 - Process: Validation
  • Segment 11 - Temperature

Quiz 1

    • 1 Quiz
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1 Quiz

  • Module 1 Quiz

Writing Assignment

    • 1 PeerReview
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1 Peer Review

  • Product tear down, build a bill of materials (BOM)

Lectures

    • 16 Videos
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16 Videos

  • Introduction
  • Segment 1 - Learning Outcomes, Introduction to Thermistors
  • Segment 2 - Terminology: Resolution, Precision, Accuracy, Tolerance
  • Segment 3 - Basic Sensor Circuit
  • Segment 4 - Accuracy Example
  • Segment 5 - Calculating Rtherm
  • Segment 6 - Validating Calibration
  • Segment 7 - Filtering Techniques
  • Segment 8 - Block, Object and Key-Value Storage Devices
  • Segment 9 - Filesystem Basics
  • Segment 10 - A File on a Hard Drive
  • Segment 11 - A File on a Solid State Drive
  • Segment 12 - File System: NFS
  • Segment 13 - How Big is "Big"?
  • Segment 14 - Traditional File System Bottlenecks
  • Segment 15 - Parallel Distributed File Systems: Hadoop, Lustre

Quiz 2

    • 1 Quiz
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1 Quiz

  • Module 2 Quiz

Lectures

    • 22 Videos
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22 Videos

  • Introduction
  • Segment 1 - Learning Outcomes
  • Segment 2 - AI Backgrounder
  • Segment 3 - Machine Learning, What is it?
  • Segment 4 - Machine Learning Schools of Thought
  • Segment 5 - Get the Tools
  • Segment 6 - Categories of Machine Learning
  • Segment 7 - Supervised Learning, Linear Regression 1
  • Segment 8 - Supervised Learning, Linear Regression 2
  • Segment 9 - Supervised Learning, Linear Regression 3
  • Segment 10 - Supervised Learning, Linear Regression 4
  • Segment 11 - Supervised Learning, Bayes Theorem
  • Segment 12 - Supervised Learning, Naive Bayes
  • Segment 13 - Supervised Learning, Support Vector Machines (SVM) Introduction
  • Segment 14 - Supervised Learning, SVMs
  • Segment 15 - Unsupervised Learning, K-Means
  • Segment 16 - Reinforcement Learning
  • Segment 17 - Supervised Learning, Deep Learning
  • Segment 18 - Rick Rashid, Natural Language Processing
  • Segment 19 - Deep Learning, Hearing Aid
  • Segment 20 - Machine Learning in IIoT
  • Segment 21 - Machine Learning Summary

Quiz 3

    • 1 Quiz
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1 Quiz

  • Module 3 Quiz

Lectures

    • 19 Videos
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19 Videos

  • Introduction
  • Segment 1 - Learning Outcomes, Definition of Big Data
  • Segment 2 - Importance of Big Data, Characteristics of Big Data
  • Segment 3 - Size of Big Data
  • Segment 4 - Introduction to Predictive Analytics
  • Segment 5 - Role of Statistics and Data Mining
  • Segment 6 - Machine Learning, Generalization and Discrimination
  • Segment 7 - Frameworks, Testing and Validating
  • Segment 8 - Bias and Variance in your Data
  • Segment 9 - Out-of-sample Data and Learning Curves
  • Segment 10 - Cross Validation
  • Segment 11 - Model Complexity, Over- and Under-fitting
  • Segment 12 - Processing Your Data Prior to Machine Learning
  • Segment 13 - Good Data, Smart Data
  • Segment 14 - Visualizing Your Data
  • Segment 15 - Principal Component Analysis (PCA)
  • Segment 16 - Prognostic Health Management, Hadoop Machine Learning Library
  • Segment 17 - My Example: Predicting NFL Football Winners
  • Segment 18 - Tom Bradicich, Hewlett Packard's Viewpoint on Big Data

Quiz 4

    • 1 Quiz
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1 Quiz

  • Module 4 Quiz

Auto Summary

Enhance your personal development with "Project Planning and Machine Learning," a comprehensive course by Coursera. Under the guidance of expert instructors, you'll master project execution, sensor calibration, and the intricacies of hard drives and file systems. Gain foundational knowledge in machine learning algorithms and big data preparation. Part of CU Boulder's Master's in Electrical Engineering, this professional-level course spans 1080 minutes and offers flexible subscription options. Ideal for professionals seeking to expand their expertise in project management and machine learning.

David Sluiter
Instructor

David Sluiter