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b/doc/LectureNotes/_build/html/_sources/schedule.md @@ -40,7 +40,7 @@ For the reading assignments we use the following abbreviations: - Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition - Video of lecture Thursday at https://youtu.be/jYdg2xzKa5E - Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition -- Video of lecture Friday at +- Video of lecture Friday at https://youtu.be/07e-SUYRzM0 - Reading recommendations: - See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html. diff --git a/doc/LectureNotes/_build/html/schedule.html b/doc/LectureNotes/_build/html/schedule.html index 48b992c41..caea6725f 100644 --- a/doc/LectureNotes/_build/html/schedule.html +++ b/doc/LectureNotes/_build/html/schedule.html @@ -537,7 +537,7 @@ const thebe_selector_output = ".output, .cell_output"

  • Thursday: Review of ordinary Least Squares with applications, reminder on statistics and start discussion of Ridge Regression and Singular Value Decomposition

  • Video of lecture Thursday at https://youtu.be/jYdg2xzKa5E

  • Friday: Discussion of Ridge and Lasso Regression and links with Singular Value Decomposition

    • -

  • Video of lecture Friday at

    • +

  • Video of lecture Friday at https://youtu.be/07e-SUYRzM0

  • Reading recommendations:

    • See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html.

      • diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js index 9c0a6c2c9..ce1ccc582 100644 --- a/doc/LectureNotes/_build/html/searchindex.js +++ b/doc/LectureNotes/_build/html/searchindex.js @@ -1 +1 @@ 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. 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Linear Regression","14. Building a Feed Forward Neural Network","15. Solving Differential Equations with Deep Learning","16. Convolutional Neural Networks","17. Recurrent neural networks: Overarching view","4. Ridge and Lasso Regression","5. Resampling Methods","6. Logistic Regression","8. Support Vector Machines, overarching aims","9. Decision trees, overarching aims","10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods","11. Basic ideas of the Principal Component Analysis (PCA)","13. Neural networks","7. Optimization, the central part of any Machine Learning algortithm","12. Clustering and Unsupervised Learning","Applied Data Analysis and Machine Learning","2. Linear Algebra, Handling of Arrays and more Python Features","Teaching schedule with links to material","1. Elements of Probability Theory and Statistical Data Analysis","Teachers and 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