Difference between revisions of "Course syllabus"
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| + | |title=Data Science Course syllabus | ||
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| + | |keywords=Data Science Course | ||
| + | |description=Below is listed the Data Science 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=== | ||
| + | #[[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: Mathematics of decision making|Mathematics of decision making]] | #[[Course syllabus: Mathematics of decision making|Mathematics of decision making]] | ||
#[[Course syllabus: Data Mining in Business Analytics|Data Mining in Business Analytics]] | #[[Course syllabus: Data Mining in Business Analytics|Data Mining in Business Analytics]] | ||
| + | #[[Course syllabus: Category theory for Machine Learning|Category theory for Machine Learning]] | ||
Latest revision as of 13:44, 3 May 2024
Below are listed the course syllabi on Data Science topics.
Course Syllabi
- Human-computer interfaces
- 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
- Category theory for Machine Learning