Explore the fundamental concepts and applications of Hidden Markov Models in this comprehensive lecture. Delve into classification techniques, examine the underlying assumptions, and understand the key tasks associated with these models. Learn about robot localization and discover how Hidden Markov Models are applied in real-world scenarios. Gain insights into monitoring tasks, hindsight reasoning, and the process of determining the most likely explanation. Enhance your understanding of this powerful probabilistic tool and its relevance in various fields of computer science and artificial intelligence.
Overview
Syllabus
Introduction
Classification
Hidden Markov Models
Assumptions
Summary
Robot Localization
Hidden Markov Model
Tasks
Monitoring Task
hindsight reasoning Task
most likely explanation
Application
Taught by
Pascal Poupart
