Neural Nets for NLP - Minimum Risk Training and Reinforcement Learning

This course covers the following learning outcomes and goals: understanding Error, Risk, and Minimum Risk Training in Neural Networks for NLP, grasping the concept of Reinforcement Learning, learning about Policy Gradient, REINFORCE, and Value-based Reinforcement Learning, and exploring methods to stabilize Reinforcement Learning. The course teaches individual skills such as implementing Policy Gradient, understanding Credit Assignment for Rewards, adding a Baseline, calculating Baselines, and estimating Value Functions in the context of Neural Networks for NLP. The teaching method of the course involves lectures and theoretical explanations on Error, Risk, Minimum Risk Training, and Reinforcement Learning, with a focus on practical applications and examples. The intended audience for this course includes students and professionals interested in Neural Networks for Natural Language Processing, specifically those looking to deepen their understanding of Minimum Risk Training and Reinforcement Learning techniques in this field.