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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 »

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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 »

Spreading the Word about (Web)Genre Research

Spreading the Word about (Web)Genre Research

What is genre? Why is it useful to master genre conventions? Can we classify document genres automatically? Around the world, lots of researches and scholars belonging to a wide range of disciplines are trying to provide answers to these and to many other questions. Aristotle suggested the first genre classification scheme by dividing literature into Tragedy, Comedy and Lyrics (well, I … Read entire article »

Working Definition of Digital Genre (II)

Last Updated: 22 June 2014 – 26 June 2014 – 3 July 2014 - — draft in progress — In this blog post (that I will update seamlessly), I would like to pin down a working definition of digital genre that is appropriate for our computational experiments. The experiments I refer to are those that will be included in the forthcoming book “Computational Theory of Digital Genre” that I have already announced a while ago. With Michael Oakes and Georgious Paltoglou (both at University of Woleverhampton, UK), we are setting up experiments focussing on the computational modeling of the concept of digital genre. Since the concept of genre is difficult to define in a simple way, because it … Read entire article »