[Tutorial] Solve 100 Problems of Math and R/Python for Statistical Learning 2/21 14:00-17:00(JST: UTC+9)
Room: RYB2

Chair: Koji Yamamoto (Yokohama City University, Japan)

• Solve 100 Problems of Math and R/Python for Statistical Learning
  Joe Suzuki (Osaka 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.
  For the details, visit
  https://bayesnet.org/books/?page_id=49
  https://bayesnet.org/books/?page_id=118
  https://bayesnet.org/books_jp/