Difference between revisions of "Step 3"

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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]  
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# Singular values decomposition and tensor product  
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# Singular values decomposition and tensor product with [https://timbaumann.info/svd-image-compression-demo/ demo]
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# The first slide
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# The second slide and the third slide
 
# Homework discussion
 
# Homework discussion
  
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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
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# Refresh in your memory the gradient-based optimization, see either:
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# Refresh in your memory the Bayesian Statistics, see either:
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## [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]
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## 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].
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<!--
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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
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-->
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<!--statistical and Bayesian inference-->
  
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<!--statistical and Bayesian inference-->
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=== Schemes for the second slide ===
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====Scheme 1====
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# Problem to research
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# Method
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# Contribution
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====Scheme 2====
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# Problem to solve
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# Problem statement
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# Soluton
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====Scheme 3====
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# Goal
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# Alternatives
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# Choise reasoning
  
 
<!--==Fun==-->
 
<!--==Fun==-->

Latest revision as of 01:01, 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

  1. The warm-up 5-minute test
  2. Singular values decomposition and tensor product with demo
  3. The second slide and the third slide
  4. Homework discussion

Resources

Step 3 YouTube video

Homework

  1. For your selected project write the motivational part using this template for Step 3.
  2. Download the link to your homework here.
  3. Also, Step 2 homework reminder
  4. Refresh in your memory the Bayesian Statistics, see either:
    1. Covariance Matrix, Multivariate Normal Distribution, Generalized Linear Model, Ridge Regression, Bayesian inference
    2. the book by C.P. Bishop, Chapter 1.

Schemes for the second slide

Scheme 1

  1. Problem to research
  2. Method
  3. Contribution

Scheme 2

  1. Problem to solve
  2. Problem statement
  3. Soluton

Scheme 3

  1. Goal
  2. Alternatives
  3. Choise reasoning


Transcript of the video

Appears after the seminar.

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