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

- 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

**Join the CompressiveSensing subreddit or the Google+ Community or the Facebook page and post there !**

Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

## No comments:

Post a Comment