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 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