Bayes Networks, Hidden Markov Models and How I Wake Up | Learning Intelligence 10

Key Takeaways

what's going on guys welcome to learning intelligence episode 10 so this morning I'd time to wake up a bit earlier than usual and it went alright and I worked out extremely tight so I figured I need to do something to wake myself up so 20 push-ups 20 jumping jacks great squats and I'm gonna give this day two quick tips for waking up so first of all move around do some movement do some push-ups do some jumping jacks do some bodyweight squats go for a walk or something but before that drink 500 Mills to a liter of water because when you sleep you're in your bed your body it sweats you get dehydrated and being dehydrated is not a good place to be so rehydrate and get moving wake up get learning that's what I'm about to do decided to head into the library today I find more and more often when you're surrounded by people in in a good environment doing this similar thing to what you are and this goes for the bad case as well like for example when you go to gym you're surrounded by people working out so you're more likely to work out when you go to the library you're surrounded by people who are studying so you're more likely to study and vice versa goes for the bad things if you're surrounded by people who are doing not-so-great activities well then you're more likely to participate in those non good activities even though at the time you might think that they're the right thing to do because everyone around you is doing the same thing but I'm gonna go to library for a few hours work through the Udacity artificial intelligence nano degree classes and look what happened to my water bottle I was getting out of the car hopelessly and it shattered everywhere I had to pick up all these shards of glass and put it into a little little cardboard box a Toma car didn't have any plastic bag or anything I picked up most of them but I couldn't get the really small ones there's some glass shards on the on the sidewalk here and I'm I'm very sorry about that I'm gonna go tell someone that's what you get for using glass water bottle just go back from the library it was actually quite packed like there wasn't many places for me to take a seat I had to do a few laps before I could find somewhere to sit down and do some study that's a good sign I like seeing people in the library and I'm glad I went because I got some really good study done like I went through the past two days have been two days where those events happen where it just deters you from what you actually wanted to do you know when off my example I set out six tasks I want to do the next day the night before with the number one priority being the first task and I don't do anything else other than the first one and to even get the first one done the past two days and so they they've carried over from the subsequent days to go into the library was a real good chance to catch up on those things I was planning to do yesterday but someone smarter than me I read somewhere said whatever you set out to plan to do in your new time frame readjust it by about 50% and that's that's an accurate goal of of where you end up unless you're really good and you just just manage to punch things in but I wrote down because I went through so many things I wrote down what what cast I went through so essentially I'm working towards the final project of term one other Udacity artificial-intelligence nanodegree which is using hidden Markov models to detect American Sign Language so different scenes different gestures in American Sign Language that's going to be really fun I'm really excited about this module actually the first class was on probability and the second class is on Bayes Nets and then though class was on inference and Bayes nets and now I'm up to about three quarters away through the hidden Markov models class and I'm listening to all the videos on 1.5 speed I go through them really quickly the first time around and then I go back through them to to resubmit knowledge that's how I've done in the past few past few modules and I'm really enjoying that process of doing it today has definitely been a productive day I've completed these four classes of probability Bayes Nets inference and Bayes nets and hidden Markov models was the one I just finished up there and now a next task is to use hmms to recognize American Sign Language but as I said before I'm going to go back through those four classes probability Bayes Nets inference and Bayes Nets and in Markov models to gain a deeper understanding as well as I've got a fair few readings that I need to get through so I probably won't start the project for at least another week or so that'll give me it'll still give me 12 days to work on the project that's the goal anyway all this talk of Bayes networks probability get a Markov models and I haven't even given an example and I just told you what I've done so I've written some notes here about what my overview of the two main concepts in in those four lectures that I went through probability Bayes Nets Bayes nets influence and peer Markov models and so first of all Bayes network or let's start a probability so probability at a very basic level is the chances of something happening what is the likely outcome of an event for example a coin toss if I toss it coin what is the likelihood that his heads and tails every time it's a 30% chance of landing on either side I do it 10 times 510 times I'll get heads 5 hour 10 times I'll get tails now that won't always happens sometimes we get 7 heads three tails eight heads to tails vice versa whatnot but the longer you do it out safer we we toss the coin a hundred thousand times it's more than likely that it's going to converge into a normal probability distribution so fifty thousand heads 50,000 tails or very close to that that ratio fifty-fifty this is a very simple overview of probability what's a Bayes Network a Bayes network is essentially a graph showing the chances of one thing you're caring based on the chances of another thing occurring let's use the Monty Hall problem as an exempt you never heard of it Monty Hall is a game show host in the classic problem is there's three doors and you can choose one door to all the doors how to go behind it I'll put a picture here so you can see and one of the doors have a car behind it so you choose door number one Monty Hall will then open up door number two and reveal it at door number two has it go behind it so now you know definitely that do one umber two has a go behind it and what the host does is he tries to trick you and say already pretends he tries to trick you do you want to switch from door number one or door number three and what is what would this like in a Bayes network when you choose door number one you have a 33% chance of choosing the car and then when you open up door number two it takes away the fact that door number two is an option anymore so now you have a choice between door number one and door number three should you stay with door number one or two switch to door number three so because door number two was opened it increases the likelihood that door number three has a car behind it by 33 percent so essentially the probability from the chances of door to go to the chances of door three long story short you should always switch because when you choose door number one and you have the option of switching to door number three switching means that you've essentially picked two doors instead of just one so the Bayes network of this might look like a graph showing you the effects of opening door number two had on the probability of door number three what's the hidden Markov model or the academic definition is a statistical tool used for modeling generative sequences characterized by a set of observer observable sequences before we talk about hidden Markov models an example of a model would be deciding or finding the likelihood of tomorrow being sunny based on today being sunny using say for example a hundred days worth of previous weather history or what's the likelihood of tomorrow being rainy based on today being sunny based on a hundred hundred days of weather history in a hidden Markov model would be say you worked in a windowless room so this room had no window and I was I had no previous weather data and I was trying to predict whether tomorrow was going to be sunny but I didn't know what today was so how could I get this information that's when I can go to other sources and I can use the fact to say for example people are walking around wearing sunglasses I can probably infer that it was sunny because people have sunglasses on similarly if they were using umbrellas I could probably infer the fact that it was raining so that's what I could get that would be my observable data and in the case of a hidden Markov model I can observe the fact that people are wearing sunglasses and using umbrellas can use that information to generate a model that's predictive of the next day's weather so that's where the hidden part comes in of hidden Markov models or hmms the fact that I can't see a previous history of weather but I can observe other data the direct information of what I'm trying to predict is hidden but there is still observable information that is related to the hidden information or the information of trying to predict that's my understanding of both those things so far Bayes Nets hidden Markov models and probability I just gave some really simple examples of course you can go as deep as you want with this like the rabbit hole keeps going with this sort of stuff it's actually quite complex once you once you get down into the deepness of it that's what makes it so interesting so exciting otherwise I'll link some Eli five documents of what I just went through that's where I learned most of this stuff from I did watch the the lectures and have information overload because it was probably at a higher level than what I'm at just yet so I use the Eli five or the explain like I'm five documents to just sort of give me that foundation knowledge and then when I go back through through the lectures for the second time hopefully it does that like that second coat of paint it makes everything all shiny and smooth guess what I'm going to Melbourne tomorrow I'm gonna pack myself get it ready it's pretty late at night it's about 8:30 or so but otherwise today I spent most of the day Eldin a video for learning intelligence episode nine so that should be up now now that you're watching learning episode 10 and before I go to bed I'm gonna finish off with a few readings from this this would definitely put me to sleep and so I'm trying to trying to take a new approach to when I'm reading before bed most the time I sort of just get in there and then I just read a few pages of something it is recently being this and this is aside from this book Game of Thrones is the longest books I've ever actually read so I'm up to book two I've watched all the TV series it's my favorite show apart from mr. robot of course but otherwise I got a fair few readings to do in here next week's goals are going to be go over the classes I went over this week or we go back over them and start getting ready for for project four so the final project in the artificial intelligence nanodegree otherwise I'm going to go pack my suit and I'm gonna be out for the weekend so this is the last clip in learning intelligence episode 10 you want to see anything in a future video at all leave a comment below otherwise thank you so much for watching and we'll catch you next week keep learning