Difference between revisions of "Step 3"
From Research management course
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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/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] | ||
+ | ## the book by C.P. Bishop, chapter 1. | ||
+ | <!-- | ||
+ | Refresh in your memory the gradient-based optimization, see either: | ||
## [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/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 | ||
## the book | ## the book | ||
− | + | --> | |
<!--statistical and Bayesian inference--> | <!--statistical and Bayesian inference--> | ||
Revision as of 00:52, 5 October 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.
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.
Transcript of the video
Appears after the seminar.