Difference between revisions of "Course syllabus"
From Research management course
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===Course Syllabi=== | ===Course Syllabi=== | ||
| − | + | #[[Course schedule|My first scientific paper]] | |
| − | + | #[[Course syllabus: Bayesian model selection|Bayesian model selection]] | |
| − | + | #[[Fundamental theorems|Fundamental theorems of Machine Learning]] | |
| − | + | #[[Mathematical forecasting]] | |
| − | + | #[[Course syllabus: Structure learning and forecasting|Structure learning and forecasting]] | |
| − | + | #[[Course syllabus: Bayesian model selection and multimodeling|Bayesian multimodeling]] | |
| − | + | #[[Course syllabus: Introduction to Machine Learning|Introduction to Machine Learning]] | |
| − | + | #[[Course syllabus: Generative deep learning|Generative deep learning]] | |
| − | + | #[[Course syllabus: Applied regression analysis|Applied regression analysis]] | |
| − | + | #[[Course syllabus: Neural architecture search|Neural architecture search]] | |
| − | + | #[[Course syllabus: Big data analysis|Big data analysis]] | |
| − | + | #[[Course syllabus: Mathematics of decision making|Mathematics of decision making]] | |
| − | + | #[[Course syllabus: Data Mining in Business Analytics|Data Mining in Business Analytics]] | |
Revision as of 00:43, 4 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
- Bayesian multimodeling
- Introduction to Machine Learning
- Generative deep learning
- Applied regression analysis
- Neural architecture search
- Big data analysis
- Mathematics of decision making
- Data Mining in Business Analytics