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.

Slides

Class
Trailer en
1 Getting started with Weka en
2 Evaluation en
3 Simple classifiers en
4 More classifiers en
5 Putting it all together en

Videos

Class Lesson YouTube Youku
Trailer en en zh
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
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
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
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
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

Subtitles

Class Lesson
Trailer en zh
1 Getting started with Weka 1 Introduction en zh
2 Exploring the Explorer en zh
3 Exploring datasets en zh
4 Building a classifier en zh
5 Using a filter en zh
6 Visualizing your data en zh
Q Questions answered en
2 Evaluation 1 Be a classifier! en zh
2 Training and testing en zh
3 Repeated training and testing en zh
4 Baseline accuracy en zh
5 Cross-validation en zh
6 Cross-validation results en zh
Q Questions answered en
3 Simple classifiers 1 Simplicity first en zh
2 Overfitting en zh
3 Using probabilities en zh
4 Decision trees en zh
5 Pruning decision trees en zh
6 Nearest neighbor en zh
Q Questions answered en
4 More classifiers 1 Classification boundaries en zh
2 Linear regression en zh
3 Classification by regression en zh
4 Logistic regression en zh
5 Support vector machines en zh
6 Ensemble learning en zh
Q Questions answered en
5 Putting it all together 1 The data mining process en zh
2 Pitfalls and pratfalls en zh
3 Data mining and ethics en zh
4 Summary en zh

Transcripts

Class Lesson
1 Getting started with Weka 1 Introduction en
2 Exploring the Explorer en
3 Exploring datasets en
4 Building a classifier en
5 Using a filter en
6 Visualizing your data en
Q Questions answered en
2 Evaluation 1 Be a classifier! en
2 Training and testing en
3 Repeated training and testing en
4 Baseline accuracy en
5 Cross-validation en
6 Cross-validation results en
Q Questions answered en
3 Simple classifiers 1 Simplicity first en
2 Overfitting en
3 Using probabilities en
4 Decision trees en
5 Pruning decision trees en
6 Nearest neighbor en
Q Questions answered en
4 More classifiers 1 Classification boundaries en
2 Linear regression en
3 Classification by regression en
4 Logistic regression en
5 Support vector machines en
6 Ensemble learning en
Q Questions answered en
5 Putting it all together 1 The data mining process en
2 Pitfalls and pratfalls en
3 Data mining and ethics en
4 Summary en