Data Mining Practical Machine Learning Tools and Techniques

Dec 28, 2012

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Credibility: Evaluating what's been learned. 2. Issues: training, testing, tuningPredicting performance: confidence limitsHoldout, cross-validation, bootstrapComparing schemes: the t-testPredicting probabilities: loss functionsCost-sensitive measuresEvaluating numeric predictionThe Minimum De

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Data Mining Practical Machine Learning Tools and Techniques

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Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 5, Evaluation

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 5, Evaluation of Data Mining by I. H. Witten, E. Frank, M. A. Hall and C. J. Pal. Credibility: Evaluating what’s been learned. Issues: training, testing, tuning Predicting performance: confidence limits

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Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 10, Deep learning

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 10, Deep learning of Data Mining by I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal. Introducing Deep Learning.

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Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 11, Beyond supervised and unsupervised learning of Data Mining by I. H. Witten, E. Frank, M. A. Hall and C. J. Pal. Semi-supervised and multi-instance learning. Semisupervised learning

485 views • 17 slides

Data Mining Practical Machine Learning Tools and Techniques

Implementation: Real machine learning schemes. Decision treesFrom ID3 to C4.5 (pruning, numeric attributes, ...)Classification rulesFrom PRISM to RIPPER and PART (pruning, numeric data, )Association RulesFrequent-pattern treesExtending linear modelsSupport vector machines and neural networks

792 views • 62 slides

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 6, Trees and Rules of Data Mining by I. H. Witten, E. Frank, M. A. Hall and C. J. Pal. Algorithms for learning trees and rules. Decision trees From ID3 to C4.5 (pruning, numeric attributes, ...)

874 views • 57 slides

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 2 of Data Mining by I. H. Witten, E. Frank and M. A. Hall. Input: Concepts, instances, attributes. Terminology What’s a concept? Classification, association, clustering, numeric prediction

623 views • 35 slides

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 3 of Data Mining by I. H. Witten, E. Frank and M. A. Hall. Output: Knowledge representation. Tables Linear models Trees Rules Classification rules Association rules Rules with exceptions

672 views • 43 slides

Data Mining (and machine learning)

Data Mining (and machine learning). DM Lecture 1: Overview of DM, and overview of the DM part of the DM&ML module Many of these slides are highly derivative of Nick Taylor’s slides used for this module in previous years. Overview of My Lectures.

404 views • 26 slides

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 3 of Data Mining by I. H. Witten, E. Frank and M. A. Hall. Input: Concepts, instances, attributes. Terminology What ’ s a concept? Classification, association, clustering, numeric prediction

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Data Mining and Machine Learning

Data Mining and Machine Learning. Lecture 1: Why data is useful, and overview of DMML:. Overview of My Lectures. http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html. Module assessment. 100% by coursework Three main items of coursework, CW 1: 30% CW 2: 40% CW 3: 30%

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Data Mining (and machine learning)

Data Mining (and machine learning). DM Lecture 7: Feature Selection. Today. Finishing correlation/regression: Feature Selection: Coursework 2. Remember how to calculate r. If we have pairs of (x,y) values, Pearson’s r is:. Interpretation of this should be obvious (?).

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Data Mining (and machine learning)

Data Mining (and machine learning). ROC curves Rule Induction Basics of Text Mining. Two classes is a common and special case. Two classes is a common and special case. Medical applications: cancer, or not? Computer Vision applications: landmine, or not?

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Data Mining (and machine learning). DM Lecture 5: Clustering. Today. (unsupervised) Clustering What and why A good first step towards understanding your data Discover patterns and structure in data Which then guides further data mining Helps to spot problems and outliers

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Data Mining (and machine learning)

Data Mining (and machine learning). DM Lecture 3: Basic Statistics and Coursework 1. Communities and Crime. Here is an interesting dataset. -- state: US state (by number) - -- county: numeric code for county -- community: numeric code for community -

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Data Mining (and machine learning)

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Machine Learning Techniques for Data Mining

Topics in Artificial Intelligence:. Machine Learning Techniques for Data Mining. B Semester 2000 Lecturer: Eibe Frank. Simplicity first. Simple algorithms often work surprisingly well Many different kinds of simple structure exist: One attribute might do all the work

391 views • 35 slides

Data Mining (and machine learning)

Data Mining (and machine learning). DM Lecture 3: Basic Statistics for data miners. Overview of My Lectures. All at: http://www.macs.hw.ac.uk/~dwcorne/Teaching/dmml.html 25/9 Overview of DM (and of these 8 lectures)

284 views • 27 slides

Data Mining (and machine learning)

Data Mining (and machine learning). Lecture 2: Some common data processing tasks. Today. About classification A simple classification method (1-NN) Normalisation of instances and fields Discretization of fields Coursework B. Some notation. A dataset has N records {R 1 ,R 2 ,…,R N },

833 views • 80 slides

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