A problem statement is a concise description of a problem, issue, or challenge that needs to be addressed. It clarifies the gap between the current state and the desired outcome, highlighting why solving it is important and who it affects. Essentially, it sets the stage for finding a solution by clearly defining the problem.
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00:00Hello guys and girlies, welcome back.
00:07In the last lecture, we have seen a framework.
00:10We have seen an existing problem definition,
00:18and we have seen a machine learning solution.
00:20This is a iterative process.
00:21You have seen this process.
00:23This is the last lecture.
00:25However, point number 1,
00:29which is the problem definition.
00:32It is necessary to define that
00:35what is the problem solving?
00:37Is it machine learning problem?
00:39Is it not?
00:41It is not that I have a non-machine learning problem
00:44to solve a problem solving.
00:49For example, you have a story about a story.
00:52A farmer has a story about it.
00:54It's a story about it.
00:56It usually takes time to open a ŃŃquilå¼ penhu package into it.
00:59ItĆ£y subscribe to a box Gabo.
01:00Don't forget the more money buying this mode of place.
01:02It isIK meaning that the bot tool takes place.
01:05The label is running in one hand.
01:08The label explains the earlier question.
01:09Do you want to beŃŃŠ²ŠµŠ½Š½ŃŠ¹?
01:11Now, when you have a double-numak, you have a double-dubkey.
01:16Double-numak will be a double-dubkey.
01:18So, this means that you don't always have a machine learning that you have to do.
01:26If you have to ask yourself, you have to ask yourself,
01:29you have to ask yourself, left, right, right, turn around,
01:34then you have to do shopping mall.
01:36Now, when you have a exact location available,
01:41a set of instructions available for a destination,
01:44then you have straight away code.
01:46AFL-CADD, it will be a problem.
01:48Why to bring it to a machine learning?
01:51You have to ask yourself,
01:53is it actually a machine learning problem?
01:56How will we know?
01:58We will see which machine learning types available are,
02:02and which problem will be solved.
02:04because this is very important.
02:06So, first of all, I have to remind you,
02:08that basically,
02:10we have machine learning three types.
02:12One is supervised,
02:14sorry for my bad handwriting,
02:16and one is unsupervised,
02:18and one is unsupervised,
02:21and one is transfer.
02:25I have to say transfer.
02:26I have to say transfer.
02:27I have to say transfer.
02:28I have to say transfer learning.
02:29So, I have to say transfer learning.
02:31I will say learning.
02:32This is learning,
02:33this is learning,
02:34and this is learning.
02:35This is learning.
02:36Okay.
02:37Now, we will have to go with it.
02:38So, three types of solutions exist.
02:42Reinforce learning.
02:43I will ask you to the back side of the reason.
02:46The reason is that,
02:47Reinforce learning,
02:49practical applications,
02:51in the real world,
02:52which we apply in the job,
02:54is very limited.
02:55So,
02:56we will have to keep a little back line
02:58which is exactly available for problem solving.
03:00We will focus on that.
03:01So,
03:02first of all,
03:03supervised learning.
03:04If we have a set of problems,
03:07then we need to know
03:08that supervised learning
03:10which problem will solve.
03:12If you have seen this,
03:13if you have seen the example,
03:14you should see.
03:15Turn on the list.
03:16So,
03:17there are two subjects.
03:19The first is classification.
03:23The second one,
03:24the first is regression.
03:29Classification and regression.
03:31The second one is regression.
03:32Now,
03:33I do have to tell you,
03:35The same thing.
03:36The other one is that
03:38you have to label some data
03:39or regression.
03:40We need to know
03:41Let's say, let's say, I have 10,000 of the data and 10,000 of the bikes.
03:45So, I have machine learning to make bikes and the cars and the cars.
03:49We have label data, we have to make it.
03:51Or then, I have regression to make a number predict.
03:55How to make it? For example, I have housing sales data.
03:58This data has to make it.
04:01Now, I have to predict the next point.
04:04So, simple, I have regression to make it.
04:06And then, it should be here and it should be here.
04:09Simple regression problem.
04:11You have a label data.
04:13And you have to predict.
04:15No problem.
04:16Classification issue.
04:19However, imagine that you are working on a company and you have a data.
04:25And the company says that there are numbers.
04:28And this data is what represents the data.
04:33So, let me tell you about this.
04:36So, why do you have a label data?
04:39Okay.
04:40And the other thing, you have a specific output.
04:43You don't have a specific output.
04:45So, what do you do?
04:46What do you do?
04:47You say, okay.
04:48So, I don't know.
04:49I don't know.
04:50What output is the output.
04:51Why don't I ask unsupervised learning?
04:54Because it is unsupervised.
04:55I don't know.
04:56I don't know.
04:57I don't know.
04:58I don't know.
04:59I don't know.
05:00You have to think of the data.
05:01But I think of the data.
05:02I am using them.
05:03And I don't know.
05:04You have to think of what you are doing.
05:05To understand.
05:06You are using it.
05:07This is a question.
05:08The data is not okay.
05:10-
05:12The data is real.
05:14There are the data.
05:16different customer
05:18or support
05:20or
05:21let's say
05:22kitchen
05:23like
05:24imagine
05:25Amazon
05:26customers
05:27data
05:28now
05:29a promotion
05:30in which
05:31kitchen
05:32things
05:33now
05:34Amazon
05:35send
05:36email
05:37of course
05:38not send
05:39because
05:4095%
05:41people
05:42only
05:43you can
05:46solve
05:47Amazon
05:48say
05:49data
05:50science
05:51why
05:52you can
05:53use
05:54data
05:55data
05:56and
05:58group
05:59which
06:00are
06:01in
06:02price
06:03range
06:04because
06:05they
06:07are
06:08unsupervised
06:09data
06:10and
06:11you can
06:13see
06:14for example
06:15here
06:16price
06:17and
06:18let's say
06:19wait
06:20item
06:21wait
06:22so
06:23some
06:24people
06:25here
06:26and
06:27some
06:28here
06:29of course
06:30these
06:31people
06:32fall
06:33category
06:34fall
06:35which
06:36is
06:37the
06:39category
06:40so
06:41you can
06:42simply
06:43say
06:4495%
06:45people
06:46send
06:47they
06:48don't
06:49send
06:50this
06:51problem
06:52is
06:53transfer
06:54learning
06:55now
06:56transfer
06:57learning
06:58to
06:59why
07:00do
07:01need
07:02to
07:03do
07:04you
07:05need
07:06to
07:07do
07:08the
07:09problem
07:10but
07:11the
07:12problem
07:13is
07:14that
07:15we
07:16need
07:17to
07:18do
07:19to
07:21totally agree
07:22this
07:23means
07:24this
07:25means
07:26this
07:27means
07:28that
07:29we
07:30have
07:31made
07:32a model
07:33image
07:34classifier
07:35to
07:37for example
07:38if
07:39a
07:40bike
07:41differentiate
07:42to
07:43why
07:44why
07:45why
07:46why
07:47why
07:48why
07:49why
07:50why
07:51why
07:52why
07:53why
07:54why
07:55why
07:56why
07:57why
07:58why
07:59why
08:00why
08:01why
08:02why
08:03why
08:04why
08:05why
08:10by
08:11Imagine
08:1220
08:14how
08:17why
08:18why
08:21why
08:22why
08:23what
08:24why
08:25why
08:28why
08:31LET
08:33And when I told you the types of machine learning, I told you that it was Reinforce learning. Reinforce learning of course is applied to games. Reinforce learning is a special thing that you have a set of rules.
08:47And I told you that I don't have data to give you always reward or penalize.
09:01For example, I told you that it's a game. If you're right, if you're right, you're right.
09:11So you're right. If you're wrong, you're wrong. If you're wrong, you're wrong.
09:17So you can see that you've got a minus one score.
09:20Okay. So you can see that you have a step in which I get one.
09:24And if you're wrong, you're right.
09:25So Reinforce learning will increase your steps.
09:29That's how you get one.
09:31So this method of google's alpha go, which I've given you for example.
09:35Chinese game is a world's difficult game.
09:39the world's top 3 player.
09:42Now the game is not like that there is a set of instructions
09:45that you have limited number of moves.
09:49The computer has a lot of memory,
09:53and the best moves you can feed.
09:55The best moves you can choose quickly,
09:57because the computational power is more than human.
09:59However, the Chinese game is not like that.
10:02There are so many moves,
10:04which are in this galaxy.
10:06Can you imagine?
10:08If you win, the intuition is above.
10:11If you win, the alpha has won,
10:13and the top player has won.
10:15How can you win?
10:16Basically, the reinforcement learning is a better example.
10:18Now I am going to wind up.
10:21In this lecture,
10:23there is no machine learning.
10:26For example,
10:28you have input and output,
10:31and output.
10:34I am not sure of the output,
10:49but I am not sure of the output.
11:03But I am not sure of the output,
11:04but I am not sure of the output.
11:06then what do you expect from this case?
11:09So, this case is classically unsupervised learning.
11:15You can use classification clustering,
11:18then you can give data cluster.
11:20You can say,
11:21you can say,
11:22you can say,
11:23you can say,
11:25you can say weight,
11:27you can say height,
11:29you can say height,
11:30you can say height.
11:32And then transfer learning,
11:34and then transfer learning,
11:35you can say that
11:37this is the case.
11:39You can see that
11:41I can solve the model nothing has to do.
11:43But you can now you can use it like this.
11:45So,
11:48you can use it.
11:50Good.
11:52We'll do it.
11:54And then,
11:55we can use it.
11:57We can use it from the memory.