EDA for Machine Learning: Slides
Lecture slides for Exploratory Data Analysis for Machine Learning.
Part 1: Foundations of EDA
| Chapter | Slides |
|---|---|
| 1. Exploratory Data Analysis | eda-slides.html |
| 2. Conditional Distributions | conditioning-slides.html |
| 3. Clustering | clustering-slides.html |
| 4. Statistical Simulation | simulation-slides.html |
| 5. Sampling and Study Design | study-design-slides.html |
| 6. Information Theory | info-theory-slides.html |
Part 2: Linear Algebra Methods
| Chapter | Slides |
|---|---|
| 7. Linear Regression | lin-reg-slides.html |
| 8. Principal Component Analysis | pca-slides.html |
| 9. Linear Discriminant Analysis | lin-discr-slides.html |
Part 3: Text Data
| Chapter | Slides |
|---|---|
| 10. Text as Data | text-as-data-slides.html |
| 11. Topic Models | topic-models-slides.html |
Part 4: Time Series Data
| Chapter | Slides |
|---|---|
| 12. Time Series Data | ts-data-slides.html |
| 13. Time Domain Methods | ts-time-domain-slides.html |
| 14. Frequency Domain Methods | ts-freq-domain-slides.html |
Part 5: Graph Data
| Chapter | Slides |
|---|---|
| 15. Graph Theory for Machine Learning | graph-theory-slides.html |
Usage
These slides are designed as visual overviews for lecture or self-study orientation. We recommend using them as the first step in the slides → workbooks → book learning sequence.
- Slides: Survey the terrain (“What am I about to learn?”)
- Workbooks: Attempt exercises (“Can I do this?”)
- Book: Read for understanding (“Why does this work?”)
How to Cite
Thrall, T. (2025). Exploratory Data Analysis for Machine Learning. https://tthrall.github.io/eda4ml/