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.
Please note, the activities are only available when the course is running, as they are hosted in Google's AppEngine, incurring extra costs.
| Class | GDrive | Alternative | |
| Trailer | en | 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 |
| Class | Lesson | YouTube | Youku | GDrive | ||
| Trailer | en | en zh | (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 | ||||
| Class | Lesson | GDrive | Alternative | ||
| Trailer | en 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 | ||
| 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 | ||
| 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.