Semantics in Language Technology: Lecture 2 – Computational Semantics, An Overview Lecture 2: Computational Semantics from Marina Santini
Category: lectures
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
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
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
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
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
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
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
Lecture 1: Introduction – The Flipped Classroom
Lecture 3: Probability Theory
Machine Learning for Language Technology 2014 – Course Schedule