Module 3: NLP and Reinforcement Learning

This module covers two critical areas of AI for human-robot interaction and autonomous learning: Natural Language Processing (NLP) and Reinforcement Learning (RL).

Key Topics in NLP

• Introduction to NLP: Text processing, and language models.
• Text Classification and Sentiment Analysis: Understanding the meaning and emotion behind text.
• Named Entity Recognition (NER): Identifying key information in text.
• Speech to Text and Text to Speech: Enabling voice-based interaction with robots.

Key Topics in Reinforcement Learning

• Introduction to Reinforcement Learning: The RL framework, agents, environments, and rewards.
• Markov Decision Processes (MDPs): Formalizing the RL problem.
• Q-Learning and Deep Q-Networks (DQNs): Value-based RL algorithms.
• Policy Gradients and Actor-Critic Methods: Policy-based RL algorithms.
• Applying RL to Robotics: Challenges and opportunities.