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marina-santini documents
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Lecture 2: From Semantics To Semantic-Oriented Applications
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Lecture 6: Ensemble Methods
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Lecture 03: Machine Learning for Language Technology - Linear Classifiers
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Lecture 2 Basic Concepts in Machine Learning for Language Technology
Technology
Lecture 2: Job Opportunities
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Lecture11 logistic regression
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Lecture 5: Bayesian Classification
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Lecture 5: Structured Prediction
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Lecture 10: SVM and MIRA
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Lecture 6: Hidden Variables and Expectation-Maximization
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Lecture 4: Statistical Inference
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Lecture 8: Decision Trees & k-Nearest Neighbors
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Lecture 3 Probability Theory
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Lecture 1 introduction To The Course: The Flipped Classroom
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Lecture 9 Perceptron
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SearchInFocus: Exploratory Study on Query Logs and Actionable Intelligence
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Lecture 7: Learning from Massive Datasets
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Lecture 02: Machine Learning for Language Technology - Decision Trees and Nearest Neighbors
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Lecture 01: Machine Learning for Language Technology - Introduction
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Lecture 4: The Weka Package
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Text analytics and R - Open Question: is it a good match?
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