Difference between revisions of "Course syllabus"
From Research management course
| Line 7: | Line 7: | ||
*[[Mathematical forecasting]] | *[[Mathematical forecasting]] | ||
*[[Course syllabus: Structure learning and forecasting|Structure learning and forecasting]] | *[[Course syllabus: Structure learning and forecasting|Structure learning and forecasting]] | ||
| − | *[[Course syllabus: Bayesian | + | *[[Course syllabus: Bayesian multimodeling]] |
| + | *[[Course syllabus: Generative deep learning|Generative deep learning]] | ||
*[[Course syllabus: Applied regression analysis|Applied regression analysis]] | *[[Course syllabus: Applied regression analysis|Applied regression analysis]] | ||
*[[Course syllabus: Neural architecture search|Neural architecture search]] | *[[Course syllabus: Neural architecture search|Neural architecture search]] | ||
Revision as of 16:43, 3 March 2023
Below are listed the course syllabi on Data Science topics.
Course Syllabi
- My first scientific paper
- Bayesian model selection
- Fundamental theorems of Machine Learning
- Mathematical forecasting
- Structure learning and forecasting
- Course syllabus: Bayesian multimodeling
- Generative deep learning
- Applied regression analysis
- Neural architecture search
- Big data analysis
- Mathematics of decision making
- Data Mining in Business Analytics