Difference between revisions of "Course syllabus"

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Below are listed the course syllabi on Data Science topics.
 
Below are listed the course syllabi on Data Science topics.
  
 
===Course Syllabi===
 
===Course Syllabi===
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#[[Course syllabus:  Human-computer interfaces|Human-computer interfaces]]
 
#[[Course schedule|My first scientific paper]]  
 
#[[Course schedule|My first scientific paper]]  
 
#[[Course syllabus: Bayesian model selection|Bayesian model selection]]
 
#[[Course syllabus: Bayesian model selection|Bayesian model selection]]
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#[[Course syllabus: Bayesian model selection and multimodeling|Bayesian multimodeling]]
 
#[[Course syllabus: Bayesian model selection and multimodeling|Bayesian multimodeling]]
 
#[[Course syllabus: Introduction to Machine Learning|Introduction to Machine Learning]]
 
#[[Course syllabus: Introduction to Machine Learning|Introduction to Machine Learning]]
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#[[Course syllabus: Machine Learning|Machine Learning]]
 
#[[Course syllabus: Generative deep learning|Generative deep learning]]
 
#[[Course syllabus: Generative deep learning|Generative deep learning]]
 
#[[Course syllabus: Applied regression analysis|Applied regression analysis]]
 
#[[Course syllabus: Applied regression analysis|Applied regression analysis]]

Revision as of 00:59, 13 February 2024

Below are listed the course syllabi on Data Science topics.

Course Syllabi

  1. Human-computer interfaces
  2. My first scientific paper
  3. Bayesian model selection
  4. Fundamental theorems of Machine Learning
  5. Mathematical forecasting
  6. Structure learning and forecasting
  7. Bayesian multimodeling
  8. Introduction to Machine Learning
  9. Machine Learning
  10. Generative deep learning
  11. Applied regression analysis
  12. Neural architecture search
  13. Big data analysis
  14. Mathematics of decision making
  15. Data Mining in Business Analytics
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