Data Mining and Exploration

DME -- Spring 2013

Instructor: Charles Sutton, <csutton@inf.ed.ac.uk>
TA: Andreea Radulescu, <a.radulescu@sms.ed.ac.uk>
Office Hours: Tuesday and Friday, 10:00 - 11:00, IF 3.26. Also by appointment. Coffee, tea, and biscuits will be provided.
Course Text (recommended): The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, Tibshirani, and Friedman. (PDF available online.)
Additional Reference (optional, also online): Principles of Data Mining by Hand, Mannila and Smyth

IMPORTANT: Prerequisites: PMR is a pre-requisite for this course, MLPR is a co-requisite for this course.

Welcome! The idea behind this course is to learn to apply modern machine learning techniques, such as those discussed in IAML, MLPR, and PMR, to real world problems. We will discuss data mining, visualization, how to evaluate your results, and a few practical algorithms for clustering and classification. A primary component of the course will be case studies: You will read (and present!) recent research papers from the machine learning and data mining literature. See the course descriptor for more information.

News

Notes for revision are now available.

Times

Lecture: Friday 14:00-15:50, 7 Bristo Square (Map), Lecture Theatre 5
Lab: (Section 1): Tue, 15:10-16:00, AT5.04
Lab: (Section 2): Tue, 16:10-17:00, AT5.04

Paper Presentation

Miniprojects

Labs

There will be three lab classes. These are intended to given a brief intro to some software packages that you might decide to use in the mini-project. They are not marked, but I encourage you to attend. Hopefully they should not be too difficult. There is no formal allocation to the two lab sessions. Simply show up to whichever of the sessions suits you.

The lab sheets will be posted below prior to the lab sessions.

Week 3 (29 Jan) : R: Visualizing data
Week 4 (5 Feb): LDA and LSA
Week 5 (12 Feb): Evaluation in R

Deadlines

29 Jan, 4pm	Choose paper and group for presentation. Submit to TA via email (see address at top of page).
11 Feb	Choose data set and group for miniproject. Submit to TA via email (see address at top of page)
1 March (in class)	Paper presentations begin.
28 Feb, 4pm	Submit progress report for miniproject.
21 Mar, 4pm	MINIPROJECT DUE. Submit to ITO. No extensions.

Lecture Schedule

N.B. Readings in the list below are examinable. The lecture slides are available from the NB discussion site.

	Date	Topic
1	18 Jan	Introduction, Overview of Data Mining , Visualizing Data
2	25 Jan	Decision trees , Ensemble methods Reading: Rob Shapire boosting tutorial (Sections 4-8 not examinable) Reading: Leo Breiman, Bagging predictors, Machine Learning, 1996 Reading: Section 9.2 of Hastie, Tibshirani, and Friedman Additional reading: Murphy, Sections 16.1, 16.2 (skip 16.2.5 and 16.2.6), 16.4.1, 16.4.3
3	1 Feb	Evaluation of Learning Algorithms Lab 1 this week (29 Jan). Readings: Fawcett, T. (2003). ROC Graphs: Notes and Practical Considerations for Researchers. HP Labs Tech Report HPL-2003-4 Dietterich, T. G., (1998). Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10 (7) 1895-1924
4	8 Feb	Applications: Topic Models Reading: HTF, Section 14.5.1 Reading: Thomas Hofmann, Probabilistic Latent Semantic Analysis, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. (1999) Reading: Blei, Ng, and Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research 3 (2003) 993-1022. Reading: Murphy, 27.3.1, 27.3.2, 27.3.3 Lab 2 this week (5 Feb).
5	15 Feb	Finish topic models. Association Rules Reading: HTF 14.2 Lab 3 this week (12 Feb).
	22 Feb	NO CLASS: Innovative Learning Week
6	1 Mar	Paper presentations
7	8 Mar	Paper presentations
8	15 Mar	Paper presentations
9	22 Mar	Paper presentations

This page is maintained by Charles Sutton.

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