Lecture 9 (27/03/2023, 1 hour): probabilistic language modeling

What is a language model? N-gram models (unigrams, bigrams, trigrams), together with their probability modeling and issues. Chain rule and n-gram estimation.

Lecture 8 (24/03/2023, 4 hours): definition of LSTM; handbook of a real-world classification problem; homework 1

More on LSTMs. Notebook on training, dev, test. Notebook on a real-world NLP problem. Assignment of Homework 1!

Lecture 7 (20/03/2023, 2 hours): more on word embeddings and RNNs

More on word embeddings. Lookup tables. Cooccurence matrices. GloVe. Stopwords. Static vs. contextualized embeddings. Different inputs and outputs for RNNs.

Lecture 6 (17/03/2023, 3 hours, E): word2vec, recurrent neural networks (RNNs), Long-Short Term Memory networks (LSMTs)

word2vec (CBOW and skipgram), PyTorch notebook on word2vec, recurrent neural networks, optimization for RNNs, Long-Short Term Memory (LSMT) networks.

Lecture 5 (13/03/2023, 2 hours, E): first hands-on with PyTorch with language detection

Recap of the Supervised Learning framework, hands on practice with PyTorch on the Language Detection Model: tensors, gradient tracking, the Dataset class, the Module class, the backward step, the training loop, evaluating a model.

Lecture 4 (10/03/2023, 2 hours, E): Machine Learning for NLP and intro to neural networks

Introduction to Supervised, Unsupervised & Reinforcement Learning. The Supervised Learning framework. From real to computational: features extraction and features vectors. Feature Engineering and inferred features. PyTorch vs Tensorflow. The perceptron model. What is Deep Learning, training weights and Backpropagation.

Lecture 3 (06/03/2023, 2.5 hours)

Introduction to classification in NLP. The task of Sentiment Analysis. Probabilistic classification. Logistic Regression and its use for classification. Explicit vs. implicit features. The cross-entropy loss function. 

 

Lecture 2 (3/3/2023, 3 hours): Introduction to NLP (2/2)

Introduction to NLP in Rome. Introduction to Natural Language Processing: understanding and generation. What is NLP? The Turing Test, criticisms and alternatives. Tasks in NLP and its importance (with examples). Key areas.

 

Lecture 1 (27/02/2023, 2 hours): Introduction to NLP (1/2)

 We gave an introduction to the course and the field it is focused on, i.e., Natural Language Processing and its challenges.