Difference between revisions of "Step 2"
From Research management course
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# [https://forms.gle/KqhRk9R6w61snAB9A Step 1 homework] reminder | # [https://forms.gle/KqhRk9R6w61snAB9A Step 1 homework] reminder | ||
# Step 2 homework | # Step 2 homework | ||
| − | # | + | # Refresh in your memory the matrix decompositions and multilinear models for the next warm-up test, either |
| − | + | ## look for [https://en.wikipedia.org/wiki/Singular_value_decomposition Singular value decomposition], [https://en.wikipedia.org/wiki/Principal_component_analysis Principal component analysis], [https://en.wikipedia.org/wiki/Tensor Tensor], [https://en.wikipedia.org/wiki/Multilinear_map Multilinear map], or | |
| + | ## do fun-reading, see [https://mml-book.github.io/book/mml-book.pdf 4.5 Singular Value Decomposition] and 10.5 PCA in High Dimensions, and see [https://klassfeldtheorie.wordpress.com/wp-content/uploads/2018/10/mathematische-methoden-310117.pdf L11-4, L11.5] | ||
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<!--==Fun==--> | <!--==Fun==--> | ||
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==Transcript of the video== | ==Transcript of the video== | ||
Appears after the seminar. | Appears after the seminar. | ||
Revision as of 19:32, 27 September 2024
What is the difference between academic and industrial research projects? It is the focus. Narrow focus boosts the quality of a project and spares time. Applied scientists connect academic and industrial parts in theory and computational experiments. To narrow an industrial project one has to make a clear implementation plan. We discuss basic questions that an analyst and an expert discuss before planning.
The seminar
- The warm-up 5-minute test
- Linear models their role in neural networks and expert mixtures
- Reporting in the academy and the industry
- Plan the project
- Game of planning: the crocodile
- If someone did homework, we discuss
Resources
Step 2 YouTube video (expected with online version)
Homework
- Step 1 homework reminder
- Step 2 homework
- Refresh in your memory the matrix decompositions and multilinear models for the next warm-up test, either
- look for Singular value decomposition, Principal component analysis, Tensor, Multilinear map, or
- do fun-reading, see 4.5 Singular Value Decomposition and 10.5 PCA in High Dimensions, and see L11-4, L11.5
Transcript of the video
Appears after the seminar.