Difference between revisions of "Step 3"
From Research management course
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== The seminar == | == The seminar == | ||
# [https://forms.gle/4SjjkGukAnBq61mX7 The warm-up 5-minute test] | # [https://forms.gle/4SjjkGukAnBq61mX7 The warm-up 5-minute test] | ||
| − | # | + | # Singular values decomposition and tensor product with [https://timbaumann.info/svd-image-compression-demo/ demo] |
| − | # The | + | # The second slide and the third slide |
# Homework discussion | # Homework discussion | ||
==Resources== | ==Resources== | ||
| − | Step 3 YouTube video | + | Step 3 YouTube [https://youtube.com/live/6yzZ_ps5IrY video] |
==Homework== | ==Homework== | ||
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# Download the link to your homework [https://forms.gle/MBkcGz6aK3LX8MFy7 here]. | # Download the link to your homework [https://forms.gle/MBkcGz6aK3LX8MFy7 here]. | ||
# Also, [https://forms.gle/v6fEGLRWkXJ6UZXi9 Step 2 homework] reminder | # Also, [https://forms.gle/v6fEGLRWkXJ6UZXi9 Step 2 homework] reminder | ||
| − | # Refresh in your memory the gradient-based optimization, see either: | + | # Refresh in your memory the Bayesian Statistics, see either: |
| − | ## [https://en.wikipedia.org/wiki/Gradient_descent Gradient descent [https://en.wikipedia.org/wiki/Conjugate_gradient_method Conjugate gradient], | + | ## [https://en.wikipedia.org/wiki/Covariance_matrix Covariance Matrix], [https://en.wikipedia.org/wiki/Multivariate_normal_distribution Multivariate Normal Distribution], [https://en.wikipedia.org/wiki/Generalized_linear_model Generalized Linear Model], [https://en.wikipedia.org/wiki/Ridge_regression#Tikhonov_regularization Ridge Regression], [https://en.wikipedia.org/wiki/Bayesian_inference Bayesian inference] |
| − | [https://en.wikipedia.org/wiki/Gradient Gradient], | + | ## the book by C.P. Bishop, [https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf Chapter 1]. |
| − | [https://en.wikipedia.org/wiki/Directional_derivative Directional derivative], | + | <!-- |
| − | [https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant Jacobian matrix], | + | Refresh in your memory the gradient-based optimization, see either: |
| − | [https://en.wikipedia.org/wiki/Hessian_matrix Hessian matrix], | + | ## [https://en.wikipedia.org/wiki/Gradient_descent Gradient descent], [https://en.wikipedia.org/wiki/Conjugate_gradient_method Conjugate gradient descent], [https://en.wikipedia.org/wiki/Gradient Gradient], [https://en.wikipedia.org/wiki/Directional_derivative Directional derivative], [https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant Jacobian matrix], [https://en.wikipedia.org/wiki/Hessian_matrix Hessian matrix], [https://en.wikipedia.org/wiki/Vector_field#Gradient_field_in_Euclidean_spaces Gradient field], or |
| − | [https://en.wikipedia.org/wiki/Vector_field#Gradient_field_in_Euclidean_spaces Gradient field], or | ||
## the book | ## the book | ||
| + | --> | ||
| + | <!--statistical and Bayesian inference--> | ||
| − | + | === Schemes for the second slide === | |
| + | ====Scheme 1==== | ||
| + | # Problem to research | ||
| + | # Method | ||
| + | # Contribution | ||
| + | ====Scheme 2==== | ||
| + | # Problem to solve | ||
| + | # Problem statement | ||
| + | # Soluton | ||
| + | ====Scheme 3==== | ||
| + | # Goal | ||
| + | # Alternatives | ||
| + | # Choise reasoning | ||
<!--==Fun==--> | <!--==Fun==--> | ||
Latest revision as of 17:16, 9 November 2024
We are moving towards the coursework. How do you put your project description in one pitch? We reformulate the deliveries from Step 1 and Step 2 in an informal problem statement.
Contents
The seminar
- The warm-up 5-minute test
- Singular values decomposition and tensor product with demo
- The second slide and the third slide
- Homework discussion
Resources
Step 3 YouTube video
Homework
- For your selected project write the motivational part using this template for Step 3.
- Download the link to your homework here.
- Also, Step 2 homework reminder
- Refresh in your memory the Bayesian Statistics, see either:
- Covariance Matrix, Multivariate Normal Distribution, Generalized Linear Model, Ridge Regression, Bayesian inference
- the book by C.P. Bishop, Chapter 1.
Schemes for the second slide
Scheme 1
- Problem to research
- Method
- Contribution
Scheme 2
- Problem to solve
- Problem statement
- Soluton
Scheme 3
- Goal
- Alternatives
- Choise reasoning
Transcript of the video
Appears after the seminar.