   Using Hidden Markov Model for designing AI mp3 player
  Hey guys.Im working on an assignment, where I want to design an AI for a mp3 player. The AI must be trained and designed with the use of a HMM method.The mp3 player shall have the functionality of adapting to its user, by analyzing incoming biological sensor data, and from this data the mp3 player will choose a genre for the next song. Given in the assignment is 14 samples of data:One sample consist of Heart Rate, Respiration, Skin Conductivity, Activity and finally the output genre. Below is the 14 samples of data, just for you to get an impression of what im talking about.<code>Sample HRRSP    SCActivity GenreS1Medium Low    High   Low RockS2High   Low    Medium HighRockS3High   High   Medium Low ClassicS4High   Medium Low    Medium   ClassicS5Medium Medium Low    Low ClassicS6Medium Low    High   HighRockS7Medium High   Medium Low ClassicS8High   Medium High   Low RockS9High   High   Low    Low ClassicS10    Medium Medium Medium Low ClassicS11    Medium Medium High   HighRockS12    Low    Medium Medium HighClassicS13    Medium High   Low    Low ClassicS14    High   Low    Medium HighRock</code>My time of work regarding HMM is quite low, so my question to you is if I got the right angle on the assignment.I have three different states for each sensor: Low, Medium, High.Two observations/output symbols: Rock, ClassicIn my own opinion I see my start probabilities as the weightened factors for either a Low, Medium or High state in the Heart Rate. So the ideal solution for the AI is that it will learn these 14 sets of samples. And when a users sensor input is received, the AI will compare the combination of states for all four sensors, with the already memorized samples. If there exist a matching combination, the AI will choose the genre, and if not it will choose a genre according to the weightened transition probabilities, while simultaniously updating the transition probabilities with the new data.Is this a right approach to take, or am I missing something ? Is there another way to determine the output probability (read about Maximum likelihood estimation by EM, but dont understand the concept)?Best regards, Casper   
  Are you sure you're supposed to use a HMM? I don't see how a HMM fits in this assignment. If you just need to predict the next genre from current sensory data you should probably use a Naive Bayes model.