Lectures

You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

• Introduction
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • Lab
  • Dataset description
  • ISLP Chapter 1
  • PSDS Chapter 1
  • Top 10 Ideas in Statistics That Have Powered the AI Revolution
  • Recorded video
• Statistical Learning
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • Lab
  • ISLP Chapter 2
  • ESL Chapter 2
  • Foundations of Data Science Chapter 2
  • Recorded video
• Regression
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • Lab
  • ISLR Chapter 3
  • ESL Chapter 3.1~3.3
  • PSDS Chapter 4 and Chapter 3
  • Ch4 in seeing theory/Ch5 in OpenIntro Statistics/p-value and hypothesis testing in statquest/04-05 in 統計學
  • Recorded video
• Classification
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • Lab
  • ISLR Chapter 4
  • ESL Chapter 4
  • PSDS Chapter 5 and Chapter 2
  • Ch3 and ch5 in seeing theory/Distribution and Bayes theorem in statquest/3Blue1Brown
  • Recorded video
• Resampling Methods
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • Lab
  • ISLR Chapter 5
  • ESL Chapter 7.1-7.4 and 7.10-7.11
  • PSDS Chapter 3
  • Recorded video
• Linear model selection and regularization
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • Lab
  • ISLR Chapter 6
  • ESL Chapter 7.1-7.7, Chapter3.3-3.4
  • PSDS Chapter 4
  • [Midterm 2023]
  • [Midterm 2022]
  • [Midterm 2021]
  • [Midterm 2020 (courtesy of professor Mei-Hui Guo)]
  • Recorded video
• Linear model selection and regularization
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • Example midterm reference
  • Recorded video