Octave, in which you'll be able to very quickly implement the the learning algorithms we've seen already, and the learning algorithms we'll see later in this course.

We saw that with the training set like our training set of housing prices and we feed that to our learning algorithm.

Given this data set, an

look at this picture it is so obvious what this is.

The question is what should you then try mixing in order to improve the learning algorithm?

By now, you see the range of different learning algorithms.

And taking gradient descent, and the square cost function, and putting them together. That will give us our first

Machine learning algorithms for data mining tasks

Within supervised learning, the performance of many supervised learning algorithms will be pretty similar and when that is less more often be whether you use

lambda.

Before you know sending the first 70 in the training set and the last 30 of the test set.

Concretely, if you have a learning algorithm that's not performing as well as you wanted it to, how can you figure out if your learning algorithm is suffering.

label examples of things that are not cars, then we give our training set to the learning algorithm trained a classifier and then, you know, we may test it and show the new image and ask, What is this new thing? .

And.

Uniformity of cell shape of the tumor, and so on, and other features as well.

And so if you see this combination, then what you find.

learn by itself to customize itself to your preferences.

So, that's it for supervised learning and in the next video I'll talk about unsupervised learning, which is the other major category of learning algorithms.

If you look back at a recent 5 or 10 year history of machine learning.

like the degree polynomial to use with the learning algorithm or choose the regularization parameter for learning algorithm.

B but when that is small there will often be things like the amount of data you are creating these algorithms on.

Which is a great prototyping language.

Let's start by looking at passive reinforcement learning.

So, congrats on learning about your first Machine Learning algorithm.

I'll tell you a little bit about that too in this class.

And we'll begin to talk about the main types of machine learning problems and algorithms.

So that when later on when you are trying to program a learning algorithms yourself, if you are trying to find a specific command that maybe you think

And so I'd like to take the next videos to talk about that.

As a result, the answer is usually less impacted by the data than it is for maximum likelihood.

But each of these would be a fine example of a

learning algorithm. So this is an example of a supervised learning algorithm.

Learning

Learn

Sometimes learning occurs online which means while the data is being generated and some of it is offline which means learning occurs after the data has been generated.

Of the following four examples, which ones, which of these four do you think would will be an Unsupervised Learning algorithm as opposed to Supervised Learning problem.

So we can do, is take these two microphone recorders and give them to an Unsupervised Learning algorithm called the cocktail party algorithm, and tell the algorithm

So let's move on to Active Reinforcement Learning and, in particular, let's examine a simple approach called a Greedy Reinforcement Learner.

We talked about Unsupervised Learning, which is a learning setting where you give the algorithm a ton of data and just ask it to find structure in the data for us.

So don't worry too much about why people call it that.

These are other types of machine learning algorithms that we'll talk about

I still have a lot more to say about bias and variance in the next few videos.

later on we'll be using this sorts of computation operations to implement our learning algorithms.

So, that's it for how to do different computational operations on the data in matrices.

There's question from Belgium from J. Vurm.

So, let's say you have a friend who tragically has a tumor. And maybe, their tumor size and age falls around there.