UL. Clustering Algorithms

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Del curso dictado por New York University Tandon School of Engineering

Fundamentals of Machine Learning in Finance

3 calificaciones

New York University Tandon School of Engineering

Fundamentals of Machine Learning in Finance

3 calificaciones

Curso 2 de 4 en SpecializationMachine Learning and Reinforcement Learning in Finance

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

De la lección

Data Visualization & Clustering

UL. Clustering Algorithms4:32

UL. K-clustering8:20

UL. K-means Neural Algorithm7:06

UL. Hierarchical Clustering Algorithms10:54

UL. Clustering and Estimation of Equity Correlation Matrix5:40

UL. Minimum Spanning Trees, Kruskal Algorithm6:40

UL. Probabilistic Clustering6:37

Conoce a los instructores

Igor Halperin

So far, in this week,

we talked about dimensional reduction in unsupervised learning.

We looked at the PCA as a linear method and then showed how it can

be generalized into a nonlinear method using a insulting whether neural network.

Now, I would like to talk about

another large cluster of Unsupervised Learning methods namely, clustering methods.

So, what is clustering and why do you need to know about it.

Generally put, clustering is a method of aggression

of you data points into relatively homogeneous groups,

such that items in the same group are more

similar to each other than items belonging in different groups.

For finance, your data points can be companies,

stocks, bonds, credit card holders, and so on.

Clustering methods can be used in many different applications.

We can use them for visualization of data and for conceptualizing the resulting clusters.

We can also use clusters for compact presentation of data.

Similar to dimension reduction.

But taking to the extreme of reducing the dimension to just one,

which happens for some types of clustering.

We can say that clustering or segmentation is one of

the most important basic tasks not only in machine learning,

but also in human learning.

Indeed, one of the first cognitive function that

a newborn learns is the ability to distinguish between me and not me.

That is the outside world.

This can be viewed as a binary clustering where me and not me,

correspond to two categories of the data that newborn's brain perceives.

So the output of a clustering method should be a discrete label for

points in the data that show the clusters to which these points belong.

Now, the way how this is done depends on the clustering method used.

There exist several dimensions by which we can classify all clustering algorithms.

First, there are two categories of clustering:

flat clustering and hierarchical clustering.

Let me explain what these terms mean.

Flat clustering is the simplest form of clustering where a set of data points

is partitioned into a relatively imagenious groups.

The word flat here means that within each cluster,

all points are equal in terms of their informational content.

All points in such cluster get

the same label which is the label that identifies this cluster.

This will be an example of the dimension reduction to just one number.

On the other hand, with hierarchical clustering,

there is some structure within each cluster,

with some sub-clusters and possibly sub sub-clusters and so on.

As we will discuss later,

many complex systems have hierarchical structure.

So hierarchical clustering methods are quite useful in practice.

A set of labels for a cluster sub-cluster and so on

provides another example of

dimension reduction that is implemented by hierarchical clustering methods.

Another difference between different types of clustering is in how they treat

cluster labels as fixed numbers so that each point

can be only in one cluster or as probability so that

each point with some probabilities can be in principle in different clusters.

The first type of clustering is called hard clustering and

the second type is called the soft clustering or probabilistic clustering.

Now, these two types of classification or

four clustering methods into flat and hierarchical,

and hard and soft are not mutually exclusive.

Therefore, a clustering method can be simultaneously flat and hard,

or hierarchical and soft.

In fact, the simplest and

the most known clustering method the K-means

clustering is exactly a flat and hard clustering method.

Let's talk about the K-means clustering next.

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