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.