Please note that all times are listed in Lisbon Time (GMT+1)
The school is divided into 3h lectures with 30min break that dive into a specific topic and 1h practical talks by leading researchers in an area. We also include basic tutorials on the mathematical and programming (Python) fundamentals needed on the first day. Due to the fact that the school is being held remotely, we have swapped the order of some lectures and practical talks to facilitate Q&A. The tentative schedule is shown below. You can also find it in Google Calendar (needs Google account) or as a static ical.zip that you can export to other calendar apps.
Students have direct access to the lectures, talks and labs through zoom and slack. Non students still have access to the stream here.
WEDNESDAY, JULY 7th
09:00 – 10:30 Morning Session 1
BASIC TUTORIALS ON PROBABILITY THEORY AND LINEAR ALGEBRA (MARIO FIGUEIREDO)
11:00 – 12:30 Morning Session 2
INTRODUCTION TO PYTHON (LUIS PEDRO COELHO)
14:00 – 17:00 Afternoon session: Introduction to the Labs and Python
17:00 – Virtual Welcome reception
THURSDAY, JULY 8th
09:00 – 12:30 Morning Lecture (30 min break at 10:30)
LECTURE 1: INTRODUCTION TO MACHINE LEARNING: LINEAR LEARNERS (ANDRE MARTINS)
- Feature representations and linear decision boundaries
- Naive Bayes, logistic regression, perceptron, SVMs
- Online learning
- Linear learning of non-linear models
14:00 – 17:00 Afternoon Labs: Linear Classifiers
17:00 – 20:30 Evening Lecture (30 min break at 18:30)
LECTURE 2: INTRODUCTION TO NEURAL NETWORKS (BHIKSHA RAJ)
- Multi-layer perceptrons (Feed Forward networks)
- Training with Backpropagation
- Connectionist Computational Models
- Universal Boolean Machines
FRIDAY, JULY 9th
09:00 – 10:00 Morning Talk
PRACTICAL TALK: ADAPTERS IN TRANSFORMERS. A NEW PARADIGM FOR TRANSFER LEARNING? (IRYNA GUREVYCH)
14:00 – 17:00 Afternoon Labs: Introduction to Deep Learning and Pytorch
17:00 – 20:30 Evening Lecture (30 min break at 18:30)
LECTURE 3: SEQUENCE MODELS (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
SATURDAY, JULY 10th
09:00 – 10:00 Morning Talk
PRACTICAL TALK: TRANSFER AND MULTI-TASK LEARNING IN NATURAL LANGUAGE PROCESSING (BARBARA PLANK)
14:00 – 17:00 Afternoon Labs: Sequence Models
17:00 – 18:00 Evening Talk
PRACTICAL TALK: HOW CAN WE KNOW WHAT AND WHEN LANGUAGE MODELS KNOW? (GRAHAM NEUBIG)
SUNDAY, JULY 11th
Free day!
MONDAY, JULY 12th
09:00 – 12:30 Morning Lecture (30 min break at 10:30)
LECTURE 4: LEARNING STRUCTURED PREDICTORS (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
14:00 – 17:00 Afternoon Labs: Structured Predictors
17:00 – 18:00 Evening Talk
PRACTICAL TALK: IS SCALE ALL WE NEED? (SLAV PETROV)
TUESDAY, JULY 13th
09:00 – 12:30 Morning Lecture (30 min coffee break at 10:30)
LECTURE 5: MODELING SEQUENTIAL DATA WITH RECURRENT NETWORKS (CHRIS DYER)
- Recurrent Neural Networks
- Learning challenges and solutions
- Conditional sequence models
- Learning with attention
14:00 – 17:00 Afternoon Labs: Sequence models in deep learning
17:00 – 18:00 Evening Talk
PRACTICAL TALK: KLUE – KOREAN LANGUAGE UNDERSTANDING EVALUATION (KYUNGHYUN CHO)
WEDNESDAY, JULY 14th
09:00 – 12:30 Morning Lecture (with 30 min coffee break at 10:30)
LECTURE 6: REINFORCEMENT LEARNING (STEFAN RIEZLER)
14:00 – 17:00 Afternoon Labs: Reinforcement Learning
17:00 – 18:00 Evening Talk
PRACTICAL TALK: EXPLOITING STRUCTURE FOR MORE EFFICIENT NLP (SASHA RUSH)
THURSDAY, JULY 15th
10:00 – 11:00 Morning Talk
PRACTICAL TALK: FAIRNESS IN MACHINE LEARNING (PEDRO SALEIRO)
14:00 – 17:30 Evening Lecture (30 min break at 15:30)
LECTURE 7: CAUSAL DATA SCIENCE – AN INTRODUCTION TO CAUSAL INFERENCE AND DATA-FUSION (ADELE RIBEIRO)
17:30 – 18:00 Closing session