Difference between revisions of "EDA-DR-Concepts"
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Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.
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Concepts of Dimension Reduction | Concepts of Dimension Reduction | ||
| + | <div style="padding:5px; margin-top:20px; margin-bottom:10px; background-color:#f2fafa; font-size:30%; font-weight:200; color: #000000; "> | ||
| + | (Concepts of Dimension Reduction) | ||
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| − | + | <b>Abstract:</b><br /> | |
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This unit discusses the "curse of dimensionality" in data mining, and introduces ideas and strategies how dimension reduction can address this problem in bioinformatics. Also: dimension reduction to create features for machine learning. | This unit discusses the "curse of dimensionality" in data mining, and introduces ideas and strategies how dimension reduction can address this problem in bioinformatics. Also: dimension reduction to create features for machine learning. | ||
<section end=abstract /> | <section end=abstract /> | ||
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| − | < | + | <b>Objectives:</b><br /> |
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| + | <b>Outcomes:</b><br /> | ||
| + | ... | ||
| + | </td> | ||
| + | </tr> | ||
| + | </table> | ||
| + | <!-- ============================ --> | ||
| + | <hr> | ||
| + | <b>Deliverables:</b><br /> | ||
| + | <section begin=deliverables /> | ||
| + | <li><b>Time management</b>: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.</li> | ||
| + | <li><b>Journal</b>: Document your progress in your [[FND-Journal|Course Journal]]. Some tasks may ask you to include specific items in your journal. Don't overlook these.</li> | ||
| + | <li><b>Insights</b>: If you find something particularly noteworthy about this unit, make a note in your [[ABC-Insights|'''insights!''' page]].</li> | ||
| + | <section end=deliverables /> | ||
| + | <!-- ============================ --> | ||
| + | <hr> | ||
| + | <section begin=prerequisites /> | ||
| + | <b>Prerequisites:</b><br /> | ||
*[[EDA-Concepts|EDA-Concepts (Concepts of Exploratory Data Analysis (EDA))]] | *[[EDA-Concepts|EDA-Concepts (Concepts of Exploratory Data Analysis (EDA))]] | ||
| + | <section end=prerequisites /> | ||
| + | <!-- ============================ --> | ||
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| − | === | + | === Evaluation === |
| − | + | <b>Evaluation: NA</b><br /> | |
| − | + | <div style="margin-left: 2rem;">This unit is not evaluated for course marks.</div> | |
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== Contents == | == Contents == | ||
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== Self-evaluation == | == Self-evaluation == | ||
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=== Question 1=== | === Question 1=== | ||
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| + | == Further reading, links and resources == | ||
| + | <!-- Formatting exqmples: | ||
| + | {{#pmid: 19957275}} | ||
| + | <div class="reference-box">[http://www.ncbi.nlm.nih.gov]</div> | ||
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| + | == Notes == | ||
| + | <references /> | ||
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*0.1 First stub | *0.1 First stub | ||
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Latest revision as of 09:27, 25 September 2020
Concepts of Dimension Reduction
(Concepts of Dimension Reduction)
Abstract:
This unit discusses the "curse of dimensionality" in data mining, and introduces ideas and strategies how dimension reduction can address this problem in bioinformatics. Also: dimension reduction to create features for machine learning.
|
Objectives: |
Outcomes: |
Deliverables:
Prerequisites:
Sorry!
This page is only a stub; it is here as a placeholder to establish the logical framework of the site but there is no significant content as yet. Do not work with this material until it is updated to "live" status.
Evaluation
Evaluation: NA
This unit is not evaluated for course marks.
Contents
Task:
- Read the introductory notes on dimension reduction and related methods for exploratory data analysis.
Self-evaluation
Further reading, links and resources
Notes
About ...
Author:
- Boris Steipe <boris.steipe@utoronto.ca>
Created:
- 2017-09-17
Modified:
- 2017-09-18
Version:
- 0.1
Version history:
- 0.1 First stub
This copyrighted material is licensed under a Creative Commons Attribution 4.0 International License. Follow the link to learn more.
Sorry!
This page is only a stub; it is here as a placeholder to establish the logical framework of the site but there is no significant content as yet. Do not work with this material until it is updated to "live" status.
