Difference between revisions of "Course syllabus"
From Research management course
| Line 4: | Line 4: | ||
*[[Course schedule|My first scientific paper]] | *[[Course schedule|My first scientific paper]] | ||
*[[Fundamental theorems|Fundamental theorems of Machine Learning]] | *[[Fundamental theorems|Fundamental theorems of Machine Learning]] | ||
| + | *[[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 model selection and multimodeling]] | *[[Course syllabus: Bayesian model selection and multimodeling]] | ||
Revision as of 21:43, 2 March 2023
Below are listed the course syllabi on Data Science topics.
Course Syllabi
- My first scientific paper
- Fundamental theorems of Machine Learning
- Mathematical forecasting
- Structure learning and forecasting
- Course syllabus: Bayesian model selection and multimodeling
- Applied regression analysis
- Neural architecture search
- Big data analysis
- Mathematics of decision making
- Data Mining in Business Analytics