Schedule

During the morning there will be lectures focusing on the main areas of ML and their application to NLP. These areas include but are not restricted to: Classification, Structured Prediction (sequences, trees, graphs), Parsing and Deep Learning.

For each topic introduced in the morning there will be a practical session in the afternoon, where students will have the opportunity to test the concepts in practice. The practical sessions will consist in implementation exercises in Python of the methods learned during the morning, testing them on real examples.

At the end of the afternoon there will be special talks of concrete applications of the these techniques being currently used in production.

All Morning Sessions and Evening Talks will be held at the Centro de Congressos. All Afternoon Labs will be held at Pavilhão de Informática. Here you can find the detailed location.

The tentative schedule is shown below.

WEDNESDAY, JULY 19TH

18:00 – 20:00 Early Bird Registration and Meet-up at Centro de Congressos
NOTE: If you can come to the early registration, do so.

THURSDAY, JULY 20TH

08:00 – 09:00 Registration at Centro de Congressos

09:00 – 10:30 Morning Session 1

BASIC TUTORIALS ON PROBABILITY THEORY AND LINEAR ALGEBRA (MARIO FIGUEIREDO)

10:30 – 11:00 Coffee Break
11:00 – 12:30 Morning Session 2

INTRODUCTION TO PYTHON (LUIS PEDRO COELHO)

12:30 – 14:00 Lunch
14:00 – 17:00 Afternoon session: Introduction to the Labs and Python

17:00 – Welcome reception
18:00 – 19:00 Discussion panel “Thinking machines: risks and opportunities”

FRIDAY, JULY 21TH

09:00 – 12:30 Morning Lecture (with 30 min coffee break at 10:30)

LECTURE 1: INTRODUCTION TO MACHINE LEARNING: LINEAR LEARNERS (STEFAN RIEZLER)

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

12:30 – 14:00 Lunch
14:00 – 17:00 Afternoon Labs: Classification
17:00 – 17:30 Coffee Break
17:30 – 18:30 Evening Talk

PRACTICAL TALK: LEARNING AND REPRESENTATION IN LANGUAGE UNDERSTANDING (FERNANDO PEREIRA)

SATURDAY, JULY 22TH

09:00 – 12:30 Morning Lecture (with 30 min coffee break at 10:30)

LECTURE 2: SEQUENCE MODELS (Slides 2016) (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

12:30 – 14:00 Lunch
14:00 – 17:00 Afternoon Labs: Sequence Models
17:00 – 17:30 Coffee Break
17:30 – 18:30 Evening Talk

PRACTICAL TALK: SMALLER, FASTER, DEEPER: UNIVERSITY OF EDINBURGH MT SUBMITTION TO WMT 2017 (ALEXANDRA BIRCH)

20:00 Summer School Banquet at Casa do Alentejo

SUNDAY, JULY 23TH

Free Day!

MONDAY, JULY 24TH

09:00 – 12:30 Morning Lecture (with 30 min coffee break at 10:30)

LECTURE 3: LEARNING STRUCTURED PREDICTORS (Slides 2016) (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

12:30 – 14:00 Lunch
14:00 – 17:00 Afternoon Labs: Structured Predictors
17:30 – 18:30 Evening Talk

PRACTICAL TALK: DEEP LEARNING FOR SPEECH RECOGNITION (MARK GALES)

TUESDAY, JULY 25TH

09:00 – 12:30 Morning Lecture (with 30 min coffee break at 10:30)

LECTURE 4: SYNTAX AND PARSING (YOAV GOLDBERG)

  • 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

12:30 – 14:00 Lunch
14:00 – 17:00 Afternoon Labs: Parsing
17:00 – 20:00 LxMLS Demo Day

WEDNESDAY, JULY 26th

09:00 – 12:30 Morning Lecture (with 30 min coffee break at 10:30)

LECTURE 5: INTRODUCTION TO NEURAL NETWORKS (BHIKSHA RAJ)

12:30 – 14:00 Lunch
14:00 – 17:00 Afternoon Labs: Introduction to Deep Learning and Theano
17:00 – 17:30 Coffee Break
17:30 – 18:30 Evening Talk

PRACTICAL TALK: SIMPLE AND EFFICIENT LEARNING WITH DYNAMIC NEURAL NETWORKS (GRAHAM NEUBIG)

THURSDAY, JULY 27th

09:00 – 12:30 Morning Lecture (with 30 min coffee break at 10:30)

LECTURE 6: MODELING SEQUENTIAL DATA WITH RECURRENT NETWORKS (Slides 2016) (CHRIS DYER)

12:30 – 14:00 Lunch
14:00 – 17:00 Afternoon Labs: Sequence models in deep learning
17:00 – 17:30 Coffee Break
17:30 – 18:30 Evening Talk

PRACTICAL TALK: TBD (KYUNGHYUN CHO)

18:30 – 19:00 Closing Remarks