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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">'''Class materials'''</div>
<div class="mainpage_boxcontents">
{| style="border: 0; margin: 0;" cellpadding="3"
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1: [[Overview class | Course overview and introduction to latent variable methods]]
 
2,3,4: [[Principal Component Analysis | Principal component analysis]] (PCA)
 
4,5: [[Process monitoring| Application of PCA to process monitoring]]
 
6: [[Projection to Latent Structures| Projection to latent structures]] (PLS)
 
7,8 [[Applications of Latent Variable Methods| Applications of latent variable methods]]: Soft sensors; Advanced preprocessing
 
9 [[Dealing with image data|Dealing with image data]]
 
10 [[Applications of Latent Variable Methods| Applications of latent variable methods]]: Classification
 
11 [[Dealing with batch data|Dealing with batch data]]
 
12 [[Multiblock data analysis| Multiblock data analysis]]: dealing with data from multiple sources
 
13 [[New product development and reformulation]]
 
14 [[Course project presentations]]
 
----
* [[Course videos| Recorded videos of all classes]]
* [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]]
* [[Course project]]
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<hr>
[[File:Crystal_Clear_mimetype_spreadsheet.png|80x80px|link=http://datasets.connectmv.com]] [http://datasets.connectmv.com All course datasets]
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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_boxcontents">
{| style="border: 0; margin: 0;" cellpadding="3"
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{|
! Class
! Topic
! Slides [[Image:Nuvola_mimetypes_pdf.png|20px]]
|-
| 1
| [[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)
|}
</div>
|}
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Latest revision as of 15:47, 20 January 2021

Latent Variable Methods for Engineering


Administrative
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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
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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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