Instructions

You should do this homework on your own -- one submission per student, and by submitting you are certifying the homework is your work.

Submission: Final submission will be via Compass (you should have been signed up automatically, if not please email Rick) Submit your answers, graphs, and other responses as a PDF

Problems

To get started please consult the lecture notes for lecture 26 and the textbook. You may ignore any data that is in a MODEL folder.

1. Obtain the activities of daily life dataset from the UC Irvine machine learning website (https://archive.ics.uci.edu/ml/datasets/Dataset+for+ADL+Recognition+with+Wrist-worn+Accelerometer data provided by Barbara Bruno, Fulvio Mastrogiovanni and Antonio Sgor-bissa).
2. Build a classifier that classifies the given files into the appropriate activity ('Use_telephone', 'Standup_chair', 'Walk', 'Climb_stairs', 'Sitdown_chair', 'Brush_teeth', 'Comb_hair', 'Eat_soup', 'Pour_water', 'Descend_stairs', 'Eat_meat', 'Drink_glass', 'Getup_bed', 'Liedown_bed'). For your classifier's features, you should use vector quantization, creating a histogram of cluster centers for each data item. You should use hierarchical k-means in order to construct the pattern vocabulary. You may use whichever multi-class classifier you wish.
3. Report your total error rate and the class confusion matrix for your classifier.
4. Then, improve your classifier by a) modifying the number of cluster centers in your hierarchical k-means and b) modifying the size of the fixed length samples that you use.
5. Hand in your source code, your total error rate and class confusion matrix for your final classifier with an explanation of how you selected your parameters and why your chosen parameters performed well.