Chair: Koji Yamamoto (Yokohama City University, Japan)
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience.
This three-hour tutorial approaches the heart of machine learning and data science by considering math problems and building R/Python source programs.
Each item mathematically formulates and solves machine learning problems and builds the programs.
Linear Regression, Classification, Resampling, Information Criteria, Sparse Estimation,
Non-Linear Regression, Decision Trees, Support Vector Machines, Unsupervised Learning.
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