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The document provides an introduction to machine learning techniques for category representation, outlining topics like clustering, classification, dimensionality reduction, and density estimation. It discusses supervised, unsupervised, and semi-supervised learning approaches and how to evaluate models using techniques like cross-validation to avoid overfitting. The goal of the course is to introduce common machine learning algorithms used in object recognition systems.

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Introduction to Machine Learning for Category Representation Jakob Verbeek November 27, 2009 Many slides adapted from S. Lazebnik
Plan for this course ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What is machine learning? ,[object Object],[object Object],[object Object]
Why machine learning? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Steps in machine learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Probability & Statistics in Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Different forms of learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Supervised learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example of classification Given: training images and their categories What are the categories of these test images?
Regression ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example of regression ,[object Object]
Regression: example 2 ,[object Object],[object Object],T. Leyvand, D. Cohen-Or, G. Dror, and D. Lischinski, Data-driven enhancement of facial attractiveness, SIGGRAPH 2008 Vector of distances v Attractiveness score f(v)
Other forms of supervised learning ,[object Object],Image Word
Structured Prediction ,[object Object],[object Object],Source: D. Ramanan model
Other supervised learning scenarios ,[object Object],Pairwise constraints Source: X. Sui, K. Grauman
Learning face similarities ,[object Object],[object Object],[object Object],[Guillaumin, Verbeek, Schmid, ICCV 2009]
Unsupervised learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Clustering example ,[object Object],[object Object],[Guillaumin, Verbeek, Schmid, ICCV 2009]
Dimension reduction ,[object Object],[object Object],[object Object]
Dimension reduction ,[object Object],[object Object],[object Object]
Dimension reduction
Topic models ,[object Object]
Topic models for images ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Density estimation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Different forms of learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semi-supervised learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],?
Example of semi-supervised learning ,[object Object],[object Object],[object Object],[Nigam et al., Machine Learning, Vol. 39, pp 103—134, 2000]
Active learning ,[object Object],[object Object],[object Object],S. Vijayanarasimhan and K. Grauman, “Cost-Sensitive Active Visual Category Learning,” 2009 
Generalization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Achieving good generalization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Bias/variance tradeoff ,[object Object]
Bias/variance tradeoff ,[object Object],[object Object],2
Bias/variance tradeoff ,[object Object],[object Object],[object Object],[object Object],2
Underfitting and overfitting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Methodology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Source: R. Parr
Plan for this course ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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slides

  • 1. Introduction to Machine Learning for Category Representation Jakob Verbeek November 27, 2009 Many slides adapted from S. Lazebnik
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  • 11. Example of classification Given: training images and their categories What are the categories of these test images?
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