AI 101 - Class Recordings-Recording Class 8

Video Transcription

Video Summary

The session began with a review of homework on bag-of-words and cosine similarity to learn vectorizing documents for frequency analysis. Bag-of-words involves creating a vocabulary of unique words and counting occurrences in documents. Then, cosine similarity was explained through calculation between document vectors using dot products and magnitudes.<br /><br />The main focus was on reinforcement learning (RL), introduced as learning through trial and error to maximize reward. RL concepts such as agents, states, actions, and rewards were explained using a snake game analogy. An agent (snake) interacts with the environment to eat apples while avoiding walls. States represent the game environment and agent position, actions define possible agent movements, and rewards indicate the gain from eating apples versus hitting walls.<br /><br />Q-learning was introduced for updating Q-values (expected rewards) to find optimal policies. By working backwards from a goal state, Q-values help determine actions maximizing cumulative rewards. State-action pairs map to Q-values, with higher ones signifying preferred actions.<br /><br />Finally, the Gini index for measuring decision tree impurity was briefly recapped. Students were reminded of the upcoming online competition, required to be without external help, promoting readiness with notes.

Keywords

bag-of-words

cosine similarity

vectorizing documents

reinforcement learning

Q-learning

agents and actions

snake game analogy

Gini index

decision tree impurity