Slides: Lisbon Machine Learning School, LXMLS 2016

Here are the slides of Lisbon Machine Learning School, (LXMLS 2016):

Basic Tutorials on Probability Theory and Linear Algebra by Mario Figueiredo 

LECTURE 1: Introduction to Machine Learning: Linear Learners by Stefan Riezler 

• Feature representations and linear decision boundaries
• Naive Bayes, logistic regression, perceptron, SVMs
• Online learning
• Linear learning of non-linear models

PRACTICAL TALK: Structured Prediction in Natural Language Processing with Imitaition Learning by Andreas Vlachos 

LECTURE 2: Sequence Models by Noah Smith 

• Markov models and hidden Markov models (HMMs)
• Dynamic programming algorithms (Viterbi and sum-product)
• Parameter learning (MLE and Baum-Welch/EM)
• Finite state machines and finite state transducers

PRACTICAL TALK: Machine Translation as Sequence Modelling by Philipp Koehn

LECTURE 3: Learning Structured Predictors by Xavier Carreras 

• From HMMs to CRFs: discriminative learning and features
• Structured perceptron, structured SVMs and max-margin Markov networks
• Training and optimization
• Iterative scaling, L-BFGS, perceptron, MIRA, stochastic and batch gradient descent

LECTURE 4: Syntax and Parsing (I, II) Slav Petrov 

• Context-free grammars (CFGs) and phrase-based parsing
• Dynamic programming and CKY algorithm
• Probabilistic CFGs, parent annotation and lexicalization
• Dependency parsing (projective and non-projective)
• Transition and graph-based parsers

PRACTICAL TALK: Turbo Parser Redux from Dependencies to Constituents by Andre Martins

LECTURE 5: Deep Neural Netowrd are our Friends by Wang Ling 

LECTURE 6: Modeling Sequential Data with Recurrent Networks by Chris Dyer

PRACTICAL TALK: Memory Networks for Language Understanding (pdf version) by Antoine Bordes 

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