AI Module 8

AustinTSchaffer · Mar 30, 2024 · 994cc28 · 994cc28
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diff --git a/OMSCS/Courses/AI/Module_08/32601581-MIT.pdf b/OMSCS/Courses/AI/Module_08/32601581-MIT.pdf
diff --git a/OMSCS/Courses/AI/Module_08/41539.pdf b/OMSCS/Courses/AI/Module_08/41539.pdf
diff --git a/OMSCS/Courses/AI/Module_08/HTS-Introduction.pdf b/OMSCS/Courses/AI/Module_08/HTS-Introduction.pdf
diff --git a/OMSCS/Courses/AI/Module_08/Module 8 - Pattern Recognition through Time.md b/OMSCS/Courses/AI/Module_08/Module 8 - Pattern Recognition through Time.md
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+---
+tags:
+  - OMSCS
+  - AI
+---
+# Module 8 - Pattern Recognition through Time
+
+This module follows chapter 14 from AIMA: [[AIMA - Chapter 14 - Probabilistic Reasoning over Time]]
+
+This module also talks through how Thad Starner applied HMMs to his research into gesture recognition. It's incredibly interesting, but I don't really know how to generalize the information, so I'm not sure how to notate it. For more info, check out Thad's PhD thesis: [[32601581-MIT.pdf]]
+
+## Warping
+- For detecting patterns which might have different y-intercepts, encode the changes in the signal's values, rather than the signal's raw values
+- For detecting patterns which might stretch and compress, do some time warping
+
+![[Pasted image 20240325101744.png]]
+
+![[Pasted image 20240325101813.png]]
+
+![[Pasted image 20240325101847.png]]
+
+## Dynamic Time Warp
+
+![[Pasted image 20240325101959.png]]
+
+Try to match values between sequences, but try to keep to the diagonal when possible.
+
+Distance in this example is $\sqrt{34}$, much lower compared to the euclidean distance of $\sqrt{170}$.
+
+## Sakoe Chiba Bounds
+![[Pasted image 20240325102302.png]]
+
+Similar to the previous algorithm, but we don't allow the dynamic warping to deviate from some bounds around the diagonal.
+
+## Hidden Markov Models
+- module focuses on 1st order HMMs
+
+![[Pasted image 20240325121112.png]]
+
+### HMMs: I vs We
+![[Pasted image 20240325121514.png]]
+
+![[Pasted image 20240325121551.png]]
+
+### Viterbi Trellis
+![[Pasted image 20240325122002.png]]
+
+![[Pasted image 20240325122317.png]]
+
+![[Pasted image 20240325122630.png]]
+
+- This is the part from the textbook chapter
+
+### New Observation for "I"
+![[Pasted image 20240325130548.png]]
+
+## New Observation for "We"
+![[Pasted image 20240325130609.png]]
+
+## Stochastic Beam Search
+![[Pasted image 20240330140601.png]]
+
+Randomly keep paths through the lattice based on the probability of those paths. The red paths are high probability paths, while the blue paths are low probability paths.
+
+## Using HMMs to Generate Data
+![[Pasted image 20240330141418.png]]
+
+> not a great idea in practice
+
diff --git a/OMSCS/Courses/AI/Module_08/chapter4.1-HMM.pdf b/OMSCS/Courses/AI/Module_08/chapter4.1-HMM.pdf
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