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1
Introduction
2
Classification
3
Hidden Markov Models
4
Assumptions
5
Summary
6
Robot Localization
7
Hidden Markov Model
8
Tasks
9
Monitoring Task
10
hindsight reasoning Task
11
most likely explanation
12
Application
Description:
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.

CS480-680 - Hidden Markov Models

Pascal Poupart
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