All the material is licensed under Creative Commons Attribution 3.0 Unported (CC-BY 3.0) and you are free to use it under that license.
Please note that the on-line activities that are part of the course are only available when the course is running and are not included below.
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.