Hidden Markov Models

Théo and Kyle discuss how hidden Markov models can be applied in unsupervised learning scenarios to infer internal states and analyze sequential behavioral patterns in various fields, such as animal behavior studies. The conversation sheds light on the versatility and descriptive power of HMMs in capturing complex patterns and behaviors.