https://noisebridge.net/index.php?title=Machine_Learning_Meetup_Notes:_2010-08-18&feed=atom&action=historyMachine Learning Meetup Notes: 2010-08-18 - Revision history2015-08-02T00:36:34ZRevision history for this page on the wikiMediaWiki 1.19.1https://noisebridge.net/index.php?title=Machine_Learning_Meetup_Notes:_2010-08-18&diff=12373&oldid=prevSpammerHellDontDelete: Created page with '====Mike - HMMs==== HMM used for time series data Markov Chains: matrix of transition probabilities: a_i_j is prob to go from state i to j the prob funciton is only a functionâ€¦'2010-08-19T04:22:31Z<p>Created page with '====Mike - HMMs==== HMM used for time series data Markov Chains: matrix of transition probabilities: a_i_j is prob to go from state i to j the prob funciton is only a functionâ€¦'</p>
<p><b>New page</b></p><div>====Mike - HMMs====<br />
HMM used for time series data<br />
<br />
Markov Chains: matrix of transition probabilities: a_i_j is prob to go from state i to j<br />
the prob funciton is only a function of the previous state<br />
<br />
HMM: example is you are trying to predict the weather based on a diary of someone's ice cream eating habits<br />
hidden state = what you are trying to predict (weather)<br />
obs state: ice cream<br />
<br />
3 problems HMMs can solve: Likelihood, Decoding, Training<br />
Likelihood: find all the paths<br />
Decoding: whats the best sequence of hidden states that produce your obs seq (Viterbi Algorithm), which is similar to Maximum Likelihood<br />
Training: Given an obs seq, learn the state transition probs and the emission probs of an HMM (Expectation Maximization, Wells-Baum, Forward-Backward Algorithm)<br />
<br />
====Thomas - HMM in R====<br />
three packages: <br />
HMM - doesnt allow for multiple chains<br />
hmm.discnp<br />
msm - allows for time based HMMs vs discrete time steps, you can fit in time inbetween states<br />
<br />
====Glen - protein prediction====<br />
<br />
[http://www.uniprot.org uniprot.org], fasta<br />
<br />
>sp|P69906|HBA_PANPA Hemoglobin subunit alpha OS=Pan paniscus GN=HBA1 PE=1 SV=2<br />
MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG<br />
KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP<br />
AVHASLDKFLASVSTVLTSKYR<br />
<br />
>sp|P69907|HBA_PANTR Hemoglobin subunit alpha OS=Pan troglodytes GN=HBA1 PE=1 SV=2<br />
MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHG<br />
KKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTP<br />
AVHASLDKFLASVSTVLTSKYR<br />
<br />
>sp|P01942|HBA_MOUSE Hemoglobin subunit alpha OS=Mus musculus GN=Hba PE=1 SV=2<br />
MVLSGEDKSNIKAAWGKIGGHGAEYGAEALERMFASFPTTKTYFPHFDVSHGSAQVKGHG<br />
KKVADALASAAGHLDDLPGALSALSDLHAHKLRVDPVNFKLLSHCLLVTLASHHPADFTP<br />
AVHASLDKFLASVSTVLTSKYR<br />
<br />
*BLOSUM62 is used for determining probabilities for protein mutation (ie. V -> I is more likely than V -> W)<br />
<br />
====Mike - Speech recognition====<br />
<br />
*fourier transform takes a wave and turns it into frequencies<br />
*spectogram - time frequency representation<br />
*speech and language processing: daniel jurafsky and james martin</div>SpammerHellDontDelete