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CS221: HMM and Particle Filters
1.
CS 221: Artificial
Intelligence Lecture 5: Hidden Markov Models and Temporal Filtering Sebastian Thrun and Peter Norvig Slide credit: Dan Klein, Michael Pfeiffer
2.
Class-On-A-Slide X 5
X 2 E 1 X 1 X 3 X 4 E 2 E 3 E 4 E 5
3.
Example: Minerva
4.
Example: Robot Localization
5.
Example: Groundhog
6.
Example: Groundhog
7.
Example: Groundhog
8.
9.
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12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
Inference in HMMs
(Filtering) E 1 X 1 X 2 X 1
28.
29.
Example HMM
30.
Example: HMMs in
Robotics
31.
32.
Example: Robot Localization
33.
34.
35.
36.
37.
38.
Particle Filters
39.
Sensor Information: Importance
Sampling
40.
Robot Motion
41.
Sensor Information: Importance
Sampling
42.
Robot Motion
43.
44.
45.
46.
47.
48.
49.
Time for Questions
50.
51.
52.
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