Shuai Huang

Machine Learning, Healthcare, and Engineering


  • Free online version here.
  • Codes (R and Python) and datasets available at github.
  • Solution manual (for instructors) available at here.
  • Buy the book here or through

book book book book

  • Suitable for introductory data analytics courses.
    • UW students, check out the Data Analytics course here.
  • Emphasize storytelling and holistic understanding
    • to build trust and transparency for trustworthy data science and AI applications.
  • Build confidence by rigorous training and simple learning strategies
    • many small datasets to guide students to work out pencil solutions and compare with established R/Python packages.
  • To train an AI-enabled workforce
    • whose members with diverse backgrounds understand the principles and intuitions of data science and AI tools
    • can communicate about them in effective language
    • can participate in the development of productive and responsible AI tools in various ways.