1. Listing up papers for Oral Qual.
I've been making a reading list for oral qualify exam. The focus of my oral exam will be (1) Folksonomies and Web 2.0 architecture, (2) Machine Learning, and (3) Multicore and parallel programming of machine learning algorithms.
2. Paper reading - HMM
I've been reading a paper about Hidden Markov Models:
L. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc IEEE, vol. 77, pp. 257-286, 1989.
A short summary is as follow:
-. HMM: A Markov chain with hidden states. HMM can be used to guess a transitions between hidden states based on the observation
-. The basic 3 problems in HMM: (a) Given a HMM model, compute probability of an observation sequence, (b) Given the observation sequence, guess a probable state sequence, and (c) Adjust HMM model parameters to maximize P(Observations|HMM Model)
-. Viterbi Algorithm can be used to solve the problem of type (b)
-. Expecation-Maximization algorithm can be applied to (c)
