diff --git a/OMSCS/Courses/AI/Module_08/32601581-MIT.pdf b/OMSCS/Courses/AI/Module_08/32601581-MIT.pdf new file mode 100644 index 0000000..565ae16 Binary files /dev/null and b/OMSCS/Courses/AI/Module_08/32601581-MIT.pdf differ diff --git a/OMSCS/Courses/AI/Module_08/41539.pdf b/OMSCS/Courses/AI/Module_08/41539.pdf new file mode 100644 index 0000000..d664e73 Binary files /dev/null and b/OMSCS/Courses/AI/Module_08/41539.pdf differ diff --git a/OMSCS/Courses/AI/Module_08/HTS-Introduction.pdf b/OMSCS/Courses/AI/Module_08/HTS-Introduction.pdf new file mode 100644 index 0000000..42f5cac Binary files /dev/null and b/OMSCS/Courses/AI/Module_08/HTS-Introduction.pdf differ 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 new file mode 100644 index 0000000..08069a9 --- /dev/null +++ b/OMSCS/Courses/AI/Module_08/Module 8 - Pattern Recognition through Time.md @@ -0,0 +1,69 @@ +--- +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 new file mode 100644 index 0000000..27e3ef0 Binary files /dev/null and b/OMSCS/Courses/AI/Module_08/chapter4.1-HMM.pdf differ diff --git a/OMSCS/Courses/AI/Module_08/images/Pasted image 20240325101744.png b/OMSCS/Courses/AI/Module_08/images/Pasted image 20240325101744.png new file mode 100644 index 0000000..db40817 Binary files /dev/null and b/OMSCS/Courses/AI/Module_08/images/Pasted image 20240325101744.png differ diff --git a/OMSCS/Courses/AI/Module_08/images/Pasted image 20240325101813.png b/OMSCS/Courses/AI/Module_08/images/Pasted image 20240325101813.png new file mode 100644 index 0000000..bedd294 Binary files /dev/null and b/OMSCS/Courses/AI/Module_08/images/Pasted image 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