Lectures
You can download the lectures here. We will try to upload lectures prior to their corresponding classes.
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Statistical Learning
tl;dr:
[Slides]
Suggested Readings:
- Lab
- ISLP Chapter 2
- ESL Chapter 2
- Foundations of Data Science Chapter 2
- Recorded video
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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
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Classification
tl;dr:
[Slides]
Suggested Readings:
- Lab
- ISLR Chapter 4
- ESL Chapter 4.1~4.4
- PSDS Chapter 5 and Chapter 2
- Ch3 and ch5 in seeing theory/Distribution and Bayes theorem in statquest/3Blue1Brown
- Recorded video
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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
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Linear model selection and regularization
tl;dr:
[Slides]
Suggested Readings:
- Lab
- ISLR Chapter 6
- ESL Chapter 7.1-7.7, Chapter 3.3-3.6
- PSDS Chapter 4
- [Midterm 2023]
- [Midterm 2022]
- [Midterm 2021]
- [Midterm 2020 (courtesy of professor Mei-Hui Guo)]
- Recorded video
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Unsupervised_learning
tl;dr:
[Slides]
Suggested Readings:
- Lab
- ISLR Chapter 12
- ESL Chapter 13.1~13.3,14.1~14.3,14.5~14.9
- Recorded video
- Recorded video 2
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Moving Beyond Linearity
tl;dr:
[Slides]
Suggested Readings:
- Lab
- ISLR Chapter 7
- ESL Chapter 5.1~5.7, 6.1~6.3 and 9.1
- PSDS Chapter 4
- Recorded video
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Tree-Based methods
tl;dr:
[Slides]
Suggested Readings:
- Lab
- ISLR Chapter 8
- ESL Chapter 9.2
- PSDS Chapter 6
- Recorded video
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Tree-Based methods
tl;dr:
Suggested Readings:
- ISLR Chapter 8
- ESL Chapter 8.1~8.3, 8.7, 8.8, 10.1~10.6, 10.8~10.14, 15.1~15.3
- PSDS Chapter 6
- XGBoost from statquest
- Recorded video
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Support_Vector_Machines
tl;dr:
[Slides]
Suggested Readings:
- Lab
- ISLR Chapter 9
- ESL Chapter 6.6~6.9,12.1~12.3
- [Machine Learning Techniques]
- Recorded video
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Final Project Presentation
tl;dr: