Exercises
Exercises for Chapters 1-10 (LMU Lecture I2ML):
| HTML | Jupyter | ||
|---|---|---|---|
| Exercise 1 | ML basics | Exercise, Solution | |
| Exercise 2 | Regression | R solution, Python solution | Exercise, Solution |
| Exercise 3 | Classification I | Exercise, Solution | |
| Exercise 4 | Classification II | R solution, Python solution | Exercise, Solution |
| Exercise 5 | Evaluation I | R solution, Python solution | Exercise, Solution |
| Exercise 6 | Evaluation II | R solution, Python solution | Exercise, Solution |
| Exercise 7 | Evaluation III | Exercise, Solution | |
| Exercise 8 | CART | R solution, Python solution | Exercise, Solution |
| Exercise 9 | Random forests | R solution, Python solution | Exercise, Solution |
| Exercise 10 | Neural networks | Exercise, Solution | |
| Exercise 11 | Tuning | R solution, Python solution | Exercise, Solution |
| Exercise 12 | Nested resampling | R solution, Python solution | Exercise, Solution |
Exercises for Chapters 11-19 (LMU Lecture SL):
- The PDF files contain the full solutions, but whenever a coding exercise is present, it is only in R and almost always the solution is outdated.
- The
Coding Exercisecolumn links to a single HTML file that contain solutions in both languages.
| Exercise Name | Coding Exercise | Google Colab | ||
|---|---|---|---|---|
| Exercise 1 | Advanced Risk Minimization 1 | Exercise, Solution | ||
| Exercise 2 | Advanced Risk Minimization 2 | Exercise, Solution | ||
| Exercise 3 | Advanced Risk Minimization 3 | Exercise, Solution | Advanced Risk Minimization 3 | R solution, Python solution |
| Exercise 4 | Multiclass Classification | Exercise, Solution | ||
| Exercise 5 | Information Theory 1 | Exercise, Solution | Information Theory 1 | R solution, Python solution |
| Exercise 6 | Information Theory 2 | Exercise, Solution | Information Theory 2 | R solution, Python solution |
| Exercise 7 | Curse of Dimensionality | Exercise, Solution | Curse of Dimensionality | R solution, Python solution |
| Exercise 8 | Regularization 1 | Exercise, Solution | Regularization 1 | R solution, Python solution |
| Exercise 9 | Regularization 2 | Exercise, Solution | Regularization 2 | R solution, Python solution |
| Exercise 10 | Support Vector Machines 1 | Exercise, Solution | Support Vector Machines | R solution, Python solution |
| Exercise 11 | Support Vector Machines 2 | Exercise, Solution | ||
| Exercise 12 | Boosting 1 | Exercise, Solution | ||
| Exercise 13 | Boosting 2 | Exercise, Solution | ||
| Exercise 14 | Feature Selection | Exercise, Solution | ||
| Exercise 15 | Gaussian Processes 1 | Exercise, Solution | ||
| Exercise 16 | Gaussian Processes 2 | Exercise, Solution | Gaussian Processes 2 | R solution, Python solution |
Exercises for Chapters 20-23 (LMU Lecture AdvML):
| Exercise | Solution | |
|---|---|---|
| Exercise 1 | ||
| Exercise 2 | ||
| Exercise 3 | ||
| Exercise 4 | ||
| Exercise 5 |