Data Mining and Exploration

DME -- Spring 2012

Instructor: Charles Sutton, <csutton@inf.ed.ac.uk>
TA: Victor Hernandez-Urbina j.v.hernandez-urbina@sms.ed.ac.uk
Office Hours: Friday 3:50 pm - 4:30 pm, or by appointment
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, DHT FRN (Faculty Room North)
Lab: (Section 1): Wed, 12:00-12:50, AT 5.04
Lab: (Section 2): Wed, 13:00-13:50, AT 5.04

Paper Presentation

Miniprojects

Labs

Week 3 (1 Feb): R: Visualizing data
Week 4 (8 Feb): LDA and LSA
Week 6 (29 Feb): Evaluation in R

Important Dates

26 Jan, 4pm	Choose paper and group for presentation. Submit to TA via email: Victor Hernandez-Urbina j.v.hernandez-urbina@sms.ed.ac.uk
1 Feb, 12-14hrs	Lab 1: Data visualisation in R.
8 Feb	Choose data set and group for miniproject. Submit to TA via email: Victor Hernandez-Urbina j.v.hernandez-urbina@sms.ed.ac.uk
8 Feb, 12-14hrs	Lab 2: LSA in R. LDA in MALLET.
27 Feb (in class)	Paper presentations begin.
29 Feb, 12-14hrs	Lab 3: Classification and evaluation in R.
1 Mar, 4pm	Submit progress report for miniproject.
29 Mar, 4pm	MINIPROJECT DUE. Submit to ITO. No extensions.

Lecture Schedule

N.B. Readings in the list below are examinable.

	Date	Topic
1	20 Jan	Introduction about Course, Overview of Data Mining [slides] Visualizing Data [slides 4up] [slides 1up] Reading: HMS Chapters 1-3
2	27 Jan	Decision trees [slides 4up] [slides 1up] Ensemble methods [slides 4up] [slides 1up] 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 Reading: HMS Section 10.5
3	3 Feb	Applications: Topic Models [slides] [slides 1up] 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.
4	10 Feb	Topic Models (cont)
5	17 Feb	Evaluation of Learning Algorithms [slides 4up] [slides 1up] 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 Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, (2008). Introduction to Information Retrieval, Cambridge University Press. Section 16.3 (only)
	24 Feb	NO CLASS: Innovative Learning Week
6	2 Mar	Collaborative Filtering Paper presentations (x2, see schedule)
7	9 Mar	Association Rules [slides] [slides 1up] Reading: HMS Chapter 13 Paper presentations (x2, see schedule)
8	16 Mar	Paper presentations (x3, see schedule)
9	23 Mar	Paper presentations (x2, see schedule)

This page is maintained by Charles Sutton and Victor Hernandez-Urbina.

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