All the material is licensed under Creative Commons Attribution 3.0 Unported (CC-BY 3.0) and you are free to use them under that license.
Slides
| Class | GDrive | Alternative | |
| Trailer | english | en | |
| 1 | Getting started with Weka | en | en |
| 2 | Evaluation | en | en |
| 3 | Simple classifiers | en | en |
| 4 | More classifiers | en | en |
| 5 | Putting it all together | en | en |
Videos
| Class | Lesson | YouTube | Youku | GDrive | ||
| Trailer | english | en zurich | (no-captions) en zh | |||
| 1 | Getting started with Weka | 1 | Introduction | en | en zh | (no-captions) en zh |
| 2 | Exploring the Explorer | en | en zh | (no-captions) en zh | ||
| 3 | Exploring datasets | en | en zh | (no-captions) en zh | ||
| 4 | Building a classifier | en | en zh | (no-captions) en zh | ||
| 5 | Using a filter | en | en zh | (no-captions) en zh | ||
| 6 | Visualizing your data | en | en zh | (no-captions) en zh | ||
| Q | Questions answered | en | ||||
| 2 | Evaluation | 1 | Be a classifier! | en | en zh | (no-captions) en zh |
| 2 | Training and testing | en | en zh | (no-captions) en zh | ||
| 3 | Repeated training and testing | en | en zh | (no-captions) en zh | ||
| 4 | Baseline accuracy | en | en zh | (no-captions) en zh | ||
| 5 | Cross-validation | en | en zh | (no-captions) en zh | ||
| 6 | Cross-validation results | en | en zh | (no-captions) en zh | ||
| Q | Questions answered | en | ||||
| 3 | Simple classifiers | 1 | Simplicity first | en | en zh | (no-captions) en zh |
| 2 | Overfitting | en | en zh | (no-captions) en zh | ||
| 3 | Using probabilities | en | en zh | (no-captions) en zh | ||
| 4 | Decision trees | en | en zh | (no-captions) en zh | ||
| 5 | Pruning decision trees | en | en zh | (no-captions) en zh | ||
| 6 | Nearest neighbor | en | en zh | (no-captions) en zh | ||
| Q | Questions answered | en | ||||
| 4 | More classifiers | 1 | Classification boundaries | en | en zh | (no-captions) en zh |
| 2 | Linear regression | en | en zh | (no-captions) en zh | ||
| 3 | Classification by regression | en | en zh | (no-captions) en zh | ||
| 4 | Logistic regression | en | en zh | (no-captions) en zh | ||
| 5 | Support vector machines | en | en zh | (no-captions) en zh | ||
| 6 | Ensemble learning | en | en zh | (no-captions) en zh | ||
| Q | Questions answered | en | ||||
| 5 | Putting it all together | 1 | The data mining process | en | en zh | (no-captions) en zh |
| 2 | Pitfalls and pratfalls | en | en zh | (no-captions) en zh | ||
| 3 | Data mining and ethics | en | en zh | (no-captions) en zh | ||
| 4 | Summary | en | en zh | (no-captions) en zh | ||
| Q | Questions answered | en | ||||
Subtitles
| Class | Lesson | GDrive | Alternative | ||
| Trailer | english zh | en zh | |||
| 1 | Getting started with Weka | 1 | Introduction | en zh | en zh |
| 2 | Exploring the Explorer | en zh | en zh | ||
| 3 | Exploring datasets | en zh | en zh | ||
| 4 | Building a classifier | en zh | en zh | ||
| 5 | Using a filter | en zh | en zh | ||
| 6 | Visualizing your data | en zh | en zh | ||
| Q | Questions answered | en | en | ||
| 2 | Evaluation | 1 | Be a classifier! | en zh | en zh |
| 2 | Training and testing | en zh | en zh | ||
| 3 | Repeated training and testing | en zh | en zh | ||
| 4 | Baseline accuracy | en zh | en zh | ||
| 5 | Cross-validation | en zh | en zh | ||
| 6 | Cross-validation results | en zh | en zh | ||
| Q | Questions answered | en | en | ||
| 3 | Simple classifiers | 1 | Simplicity first | en zh | en zh |
| 2 | Overfitting | en zh | en zh | ||
| 3 | Using probabilities | en zh | en zh | ||
| 4 | Decision trees | en zh | en zh | ||
| 5 | Pruning decision trees | en zh | en zh | ||
| 6 | Nearest neighbor | en zh | en zh | ||
| Q | Questions answered | en | en | ||
| 4 | More classifiers | 1 | Classification boundaries | en zh | en zh |
| 2 | Linear regression | en zh | en zh | ||
| 3 | Classification by regression | en zh | en zh | ||
| 4 | Logistic regression | en zh | en zh | ||
| 5 | Support vector machines | en zh | en zh | ||
| 6 | Ensemble learning | en zh | en zh | ||
| Q | Questions answered | en | en | ||
| 5 | Putting it all together | 1 | The data mining process | en zh | en zh |
| 2 | Pitfalls and pratfalls | en zh | en zh | ||
| 3 | Data mining and ethics | en zh | en zh | ||
| 4 | Summary | en zh | en zh | ||
Transcripts
| Class | Lesson | GDrive | Alternative | ||
| 1 | Getting started with Weka | 1 | Introduction | en | en |
| 2 | Exploring the Explorer | en | en | ||
| 3 | Exploring datasets | en | en | ||
| 4 | Building a classifier | en | en | ||
| 5 | Using a filter | en | en | ||
| 6 | Visualizing your data | en | en | ||
| Q | Questions answered | en | en | ||
| 2 | Evaluation | 1 | Be a classifier! | en | en |
| 2 | Training and testing | en | en | ||
| 3 | Repeated training and testing | en | en | ||
| 4 | Baseline accuracy | en | en | ||
| 5 | Cross-validation | en | en | ||
| 6 | Cross-validation results | en | en | ||
| Q | Questions answered | en | en | ||
| 3 | Simple classifiers | 1 | Simplicity first | en | en |
| 2 | Overfitting | en | en | ||
| 3 | Using probabilities | en | en | ||
| 4 | Decision trees | en | en | ||
| 5 | Pruning decision trees | en | en | ||
| 6 | Nearest neighbor | en | en | ||
| Q | Questions answered | en | en | ||
| 4 | More classifiers | 1 | Classification boundaries | en | en |
| 2 | Linear regression | en | en | ||
| 3 | Classification by regression | en | en | ||
| 4 | Logistic regression | en | en | ||
| 5 | Support vector machines | en | en | ||
| 6 | Ensemble learning | en | en | ||
| Q | Questions answered | en | en | ||
| 5 | Putting it all together | 1 | The data mining process | en | en |
| 2 | Pitfalls and pratfalls | en | en | ||
| 3 | Data mining and ethics | en | en | ||
| 4 | Summary | en | en | ||
Music
| Artist | Title | GDrive |
| Woodside Clarinets | Divertimento No. 2 Movement 1 - Allegro | mp3 |
| Divertimento No. 2 Movement 2 - Menuetto | mp3 | |
| Divertimento No. 2 Movement 3 - Larghetto | mp3 | |
| Divertimento No. 2 Movement 4 - Menuetto | mp3 | |
| Teresa Connors | Opening | mp3 |
| Incidental | mp3 | |
| Closing | mp3 |
Data Mining with Weka is brought to you by the Department of Computer Science at the University of Waikato, New Zealand.
