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<div id="mainpage_pagetitle">'''Latent Variable Methods for Engineering''': CHE 765</div>
<div id="mainpage_pagetitle">'''Latent Variable Methods for Engineering'''</div>


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<div class="mainpage_hubtitle">'''Administrative'''</div>
<div class="mainpage_hubtitle">'''Administrative'''</div>
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* [[Course outline | Course outline]]
* [[Course outline | Course outline]]
* [[Course location and hours | Location and hours]]
* [[Course location and hours | Location and hours]]
* [[Course videos| Course videos ]]
* [[Course videos| Course videos]]
* [[Latent_Variable_Methods_in_Engineering:About | Copyright and other legal stuff]]
* [[Latent_Variable_Methods_in_Engineering:About | Copyright and other legal stuff]]
<hr>
<hr>
'''Announcements'''
'''Announcements'''
* [[Course videos| Course videos are now available ]]  
* The teaching part of the course for 2011 is complete. You can view all the [[Course videos | class videos here]].
* [[Principal_Component_Analysis | Preparation for next class]] --  get ready for some math: we're going in-depth to understand exactly what \( \mathbf{T = XP} \) means
* Thanks for all the valuable feedback on this course. I'm looking forward to teaching this course again soon.
* [http://prosensus.ca/software/promv/download Link to software download] (version 11.08)
* [[Overview_class | Review on your own]] the last section of the slides on data visualization
* '''[[Principal_Component_Analysis| Course notes for 16 September]]''' are ready for download
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<div class="mainpage_hubtitle">'''Class materials'''</div>
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1: [[Overview class | Course overview and introduction to latent variable methods]]
2,3: [[Principal Component Analysis | Principal component analysis]] (PCA)
4: [[Process monitoring| Application of PCA to process monitoring]]
5: [[Projection to Latent Structures| Projection to latent structures]] (PLS)
6,7,8 [[Applications of Latent Variable Methods| Applications of latent variable methods]]
9,10 [[Dealing with 3D data|Dealing with 3D data]]: batch and image data
11 [[Multiblock data analysis| Multiblock data analysis]]: dealing with data from multiple sources
12 [[Optimization and new product development]]
13 [[Course project presentations]]
----
* [[Review_of_core_concepts | Review of basic statistical concepts]]
* [http://literature.connectmv.com/ References and readings]<br>
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<div class="mainpage_hubtitle">'''Assignments, projects and datasets'''</div>
<div class="mainpage_hubtitle">'''Assignments, projects and datasets'''</div>
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'''Assignments'''
'''Assignments'''
* Assignment 1
* [[Assignment&nbsp;1]]
* Assignment 2
* [[Assignment&nbsp;2]]
* Assignment 3
* [[Assignment&nbsp;3]]
* Assignment 4
* [[Assignment&nbsp;4]]
<hr>
<hr>
'''Project'''
'''Project'''
* Course project description
* [[Course project]]
<hr>
<hr>
[[File:Crystal_Clear_mimetype_spreadsheet.png|80x80px|link=http://datasets.connectmv.com]] [http://datasets.connectmv.com All course datasets]
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{| style="border: 0; margin: 0;" cellpadding="3"
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* [https://openmv.net All course datasets]
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<div class="mainpage_hubtitle">'''Class materials, datasets and video'''</div>
<div class="mainpage_hubtitle">'''Course calendar'''</div>
<div class="mainpage_boxcontents">
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{{#widget:Google Calendar
{| style="border: 0; margin: 0;" cellpadding="3"
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! Class
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! Slides [[Image:Nuvola_mimetypes_pdf.png|20px]]
|view=AGENDA
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| 1
|border=false
| [[Overview class | Course overview and introduction to latent variable methods]]
}}
| [[Media:Lvm-class-1.pdf|Class 1 ]]
|-
| 2, 3
| [[Principal Component Analysis | Principal component analysis]] (PCA)
| [[Media:Lvm-class-2.pdf|Class 2 ]],  [[Media:Lvm-class-3.pdf|Class 3 ]]
|-
| 4, 5
| [[Process monitoring| Application of PCA to process monitoring]]
| [[Media:Lvm-class-4.pdf|Class 4 ]],  [[Media:Lvm-class-5.pdf|Class 5 ]]
|-
| 6, 7
| [[Projection to Latent Structures| Projection to latent structures]] (PLS)
| [[Media:Lvm-class-6.pdf|Class 6 ]],  [[Media:Lvm-class-7.pdf|Class 7 ]]
|-
| 7, 8
| [[Applications of Latent Variable Methods| Applications of latent variable methods]]: Soft sensors; Advanced preprocessing
| [[Media:Lvm-class-7.pdf|Class 7 ]],  [[Media:Lvm-class-8.pdf|Class 8 ]]
|-
| 9
| [[Dealing with image data|Dealing with image data]]
| [[Media:Lvm-class-9.pdf|Class 9 ]]
|-
| 10
| [[Applications of Latent Variable Methods| Applications of latent variable methods]]: Classification
| [[Media:Lvm-class-10.pdf|Class 10 ]]
|-
| 11
| [[Dealing with batch data|Dealing with batch data]]
| [[Media:Lvm-class-11.pdf|Class 11 ]]
|-
| 12
| [[Multiblock data analysis| Multiblock data analysis]]: dealing with data from multiple sources
| [[Media:Lvm-class-12.pdf|Class 12 ]]
|-
| 13
| [[New product development and reformulation]]
| [[Media:Lvm-class-13.pdf|Class 13 ]]
|-
| 14
| [[Course project presentations]]
|}
----
* [[Course videos| Recorded videos of all classes]]
* [https://learnche.org/literature References and readings]<br>
* [[Image:Nuvola_mimetypes_pdf.png|20px]]  A <b>freely available</b> textbook for this course: [http://learnche.org/pid Process Improvement using Data] (ch 6)
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Latest revision as of 15:47, 20 January 2021

Latent Variable Methods for Engineering


Administrative
Crystal Clear action configure.png

Announcements

  • The teaching part of the course for 2011 is complete. You can view all the class videos here.
  • Thanks for all the valuable feedback on this course. I'm looking forward to teaching this course again soon.
Assignments, projects and datasets
Crystal Clear app kedit.png

Assignments

Project

Crystal Clear mimetype spreadsheet.png
Class materials, datasets and video
Crystal Clear app korganizer.png
Class Topic Slides Nuvola mimetypes pdf.png
1 Course overview and introduction to latent variable methods Class 1
2, 3 Principal component analysis (PCA) Class 2 , Class 3
4, 5 Application of PCA to process monitoring Class 4 , Class 5
6, 7 Projection to latent structures (PLS) Class 6 , Class 7
7, 8 Applications of latent variable methods: Soft sensors; Advanced preprocessing Class 7 , Class 8
9 Dealing with image data Class 9
10 Applications of latent variable methods: Classification Class 10
11 Dealing with batch data Class 11
12 Multiblock data analysis: dealing with data from multiple sources Class 12
13 New product development and reformulation Class 13
14 Course project presentations
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