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Lecture 10: SVM and MIRA

Course website: Machine Learning for Language Technology Lecture 10: SVM and MIRA from Marina Santini Outline: margin, margin infused relaxed algorithm, maximizing margin, mira, support vectors machines, svm, the norm Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »

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Lecture 9: Perceptron

Course website: Machine Learning for Language Technology Lecture 9 Perceptron from Marina Santini Outline: feature representation, main theorem, margin and separability, perceptron Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »

Lecture 6: Hidden Variables and Expectation-Maximization

Lecture 6: Hidden Variables and Expectation-Maximization from Marina Santini Outline Maximum Likelihood Estimation, Hidden and Latent Variables, Expectation-Maximization, EM for Naive Bayes Course Website: http://stp.lingfil.uu.se/~santinim/ml/2014/ml4lt_2014.htm Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »

Lecture 8: Decision Trees and k-Nearest Neighbors

Lecture 8: Decision Trees & k-Nearest Neighbors from Marina Santini Outline Decision trees, best split, entropy, information gain, gain ratio, k-Nearest Neighbors, distance metric. Course Website: http://stp.lingfil.uu.se/~santinim/ml/2014/ml4lt_2014.htm Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »

Lecture 7: Hidden Markov Models

Lecture 7: Hidden Markov Models (HMMs) from Marina Santini Outline: Hidden Markov Models (HMMs), Markov Assumptions, Problems for HMMs, Algorithms for HMMs, POS Tagging with HMMs, Smoothing for POS Tagging Course Website: http://stp.lingfil.uu.se/~santinim/ml/2014/ml4lt_2014.htm Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »

Lecture 5 Bayesian Classification

Lecture 5: Bayesian Classification from Marina Santini Outline: Bayesian Classification, Instance Attributes, Naive Bayes Classifiers, Naive Bayes in NLP, Spam Filtering Course Website: http://stp.lingfil.uu.se/~santinim/ml/2014/ml4lt_2014.htm Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »

Lecture 4: Statistical Inference

Lecture 4: Statistical Inference from Marina Santini Outline: stochastic variables, frequency functions, expectations, variance, entropy, joint probabilities, conditional probabilities, independence, sampling, estimation, maximum likelihood estimation (MLE), smoothing, hypothesis testing,z-test. http://stp.lingfil.uu.se/~santinim/ml/2014/ml4lt_2014.htm Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »

Lecture 1: Introduction – The Flipped Classroom

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Lecture 3: Probability Theory

Machine Learning for Language Technology 2014 – Course Schedule Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »

Lecture 2: Basic Concepts in Machine Learning for Language Technology

Machine Learning for Language Technology 2014 – Course Schedule Bookmark on Delicious Recommend on Facebook Share on Linkedin Tweet about it Subscribe to the comments on this post … Read entire article »