Data Mining Principles TA Session 1 (January 15, 2021)

Abour the course

  • Intro

  • Course

  • Homework and Final Project

  • Questions

  • Additional ideas to cover

  • Sources

  • How to succeed in this course


  • A great intro to ML (ML, DL and IR, AML)

  • Supervised and unsupervised learning (first part)

  • Other topics (second part)

Homework and Final Project

  • I do not grade, but happy to help

  • Hanwen Serena Xu is a grader

  • Ideas about the final project

Questions for me

  • Please send in advance, I will be able to dig deeper

  • Feel free to bring ideas what to cover more

  • Please provide feedback (what works and what does not)

Additional ideas

  • Specific algorithms?

  • Auto ML?

How to succeed in this course?

  • Push beyond your limits

  • Bring new ideas and share in your class

  • Ask great questions

  • Build a strong team

  • Build your reputation


  • Hands-On Machine Learning with R (Boehmke & Greenwell)

  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Geron)

  • Applied Predictive Modeling (Johnson & Kuhn)

  • An Introduction to Statistical Learning: With Applications in R (James et al.)

  • Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions (Taddy)

  • The Hundred-Page Machine Learning Book (Burkov)