'Training error' presentation slideshows

Robust real-time face detection

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Photo by Carl Warner

Photo by Carl Warner. Photo by Carl Warner. Photo by Carl Warner. 09/26/11. Machine Learning: Classification. Computer Vision CS 143, Brown James Hays. Slides from Lazebnik , Hoiem , and others. The machine learning framework.

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Model Assessment and Selection

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Model generalization

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Mid-Term Exam

Mid-Term Exam. Next Wednesday Perceptrons Decision Trees SVMs Computational Learning Theory In class, closed book. PAC Learnability. Consider a concept class C defined over an instance space X (containing instances of length n ),

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Introduction to Machine Learning

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Decision trees

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Molecular Diagnosis Florian Markowetz & Rainer Spang Courses in Practical DNA Microarray Analysis

Molecular Diagnosis Florian Markowetz & Rainer Spang Courses in Practical DNA Microarray Analysis. Questions in medical research: Basic Research: Which role plays gene A in disease B ? Clinical Routine: Which consequence has expression status X of gene A for patient Y ?.

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Regression

Regression. Disclaimer: This PPT is modified based on Dr. Hung- yi Lee http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17.html. Regression: Output a scalar. Stock Market Forecast Self-driving Car Product Recommendation (eg: Amazon). Dow Jones Industrial Average at tomorrow.

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Presentation Overview

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Lecture 8 Feature Selection Bioinformatics Data Analysis and Tools

C. E. N. T. E. R. F. O. R. I. N. T. E. G. R. A. T. I. V. E. B. I. O. I. N. F. O. R. M. A. T. I. C. S. V. U. Lecture 8 Feature Selection Bioinformatics Data Analysis and Tools. Elena Marchiori (elena@few.vu.nl). Why select features.

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Boosting Methods

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Advanced Machine Learning & Perception

Advanced Machine Learning & Perception. Instructor: Tony Jebara. Boosting. Combining Multiple Classifiers Voting Boosting Adaboost Based on material by Y. Freund, P. Long & R. Schapire. Combining Multiple Learners. Have many simple learners Also called base learners or

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Recognition: A machine learning approach

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Chapter 7: Model Selection

Chapter 7: Model Selection. Bias Variance Tradeoff Optimism of Training Error Estimates of In-sample Prediction Error BIC VC Dimension Bootstrap.

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INFO331 Machine learning. Neural networks. Supervised learning in neural networks.MLP and BP

INFO331 Machine learning. Neural networks. Supervised learning in neural networks.MLP and BP. (Text book: section 2.11, pp.146-155; section 3.7.3., pp.218-221); section 4.2, pp.267-282;catch-up reading: pp.251-266). Machine learning. Issues in machine learning

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Boosting

Thanks to Citeseer and : A Short Introduction to Boosting. Yoav Freund, Robert E. Schapire, Journal of Japanese Society for Artificial Intelligence,14(5):771-780, September, 1999. Boosting. Feb 18, 2008 10-601 Machine Learning. 1936 - T.

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