Lectures

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

• Introduction
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • 統計學習與資料探勘導論
  • 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:

  • 統計學習的基本框架
  • Lab
  • ISLP Chapter 2
  • ESL Chapter 2
  • Foundations of Data Science Chapter 2
  • [Recorded video]
• Regression
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • 線性迴歸（自學教材）
  • Lab
  • ISLP Chapter 3
  • ESL Chapter 3.1~3.3
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 4 and Chapter 3
  • 從樣本推論母體（自學教材）/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:

  • 分類（自學教材）
  • Lab
  • ISLP Chapter 4
  • ESL Chapter 4.1~4.4
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 5 and Chapter 2
  • 統計先備自學：分布與抽樣／貝氏推論
  • StatQuest：機率分布與貝氏定理
  • 3Blue1Brown：機率
  • [Recorded video]
• Resampling Methods
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • 重抽樣方法（自學教材）
  • Lab
  • ISLP Chapter 5
  • ESL Chapter 7.1~7.4 and 7.10~7.11
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 3
  • [Recorded video]
• Linear Model Selection and Regularization
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • 線性模型選擇與正規化（自學教材）
  • Lab
  • ISLP Chapter 6
  • ESL Chapter 7.1~7.7 and 3.3~3.6
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 4
  • [Recorded video]
  • [Midterm 2025 (A)]
  • [Midterm 2025 (B)]
  • [Midterm 2023]
  • [Midterm 2022]
  • [Midterm 2021]
• Linear Model Selection and Regularization & Recap
  tl;dr:
  [Slides] [Recap]
  

  Suggested Readings:

  • 線性模型選擇與正規化（自學教材）
  • Lab
  • ISLP Chapter 6
  • ESL Chapter 3.3~3.6 and 18.1
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 4
  • [Recorded video]
• Moving Beyond Linearity
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • 超越線性（自學教材）
  • Lab
  • ISLP Chapter 7
  • ESL Chapter 5.1~5.7, 6.1~6.3 and 9.1
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 4
  • [Recorded video]
• Tree-Based Methods
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • 樹狀方法與集成學習（自學教材）
  • Lab
  • ISLP Chapter 8
  • ESL Chapter 9.2
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 6
• Tree-Based Methods
  tl;dr:
  
  

  Suggested Readings:

  • 樹狀方法與集成學習（自學教材）
  • ISLP Chapter 8
  • ESL Chapter 8.7~8.8, 10.1~10.14 and 15.1~15.3
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 6
  • StatQuest：XGBoost
• Support Vector Machines
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • 支持向量機（自學教材）
  • Lab
  • ISLP Chapter 9
  • ESL Chapter 6.6~6.9 and 12.1~12.3
  • 林軒田：Machine Learning Techniques
• Unsupervised Learning
  tl;dr:
  [Slides]
  

  Suggested Readings:

  • 非監督式學習（自學教材）
  • Lab
  • ISLP Chapter 12
  • ESL Chapter 13.1~13.3, 14.1~14.3 and 14.5~14.9
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 7
• Unsupervised Learning
  tl;dr:
  
  

  Suggested Readings:

  • 非監督式學習（自學教材）
  • ISLP Chapter 12
  • ESL Chapter 13.1~13.3, 14.1~14.3 and 14.5~14.9
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 7
• Deep Learning
  tl;dr:
  
  

  Suggested Readings:

  • 深度學習（自學教材）
  • Lab（ISLP 官方）
  • ISLP Chapter 10
  • ESL Chapter 11
  • AI-Assisted Statistics for Data Scientists（第三版）：Chapter 8
• Final Project Presentation
  tl;dr:
  
  

  Notice:

  • Final Project