Difference between revisions of "Course syllabus"
From Research management course
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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]] | ||
| + | #[[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 19:30, 6 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
- Machine Learning
- Generative deep learning
- Applied regression analysis
- Neural architecture search
- Big data analysis
- Mathematics of decision making
- Data Mining in Business Analytics