This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. 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.
Specialization Objectives
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Из курса от партнера New York University Tandon School of Engineering
Guided Tour of Machine Learning in Finance
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New York University Tandon School of Engineering
172 оценки
Курс 1 из 4 — Specialization Machine Learning and Reinforcement Learning in Finance
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Artificial Intelligence & Machine Learning
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Igor Halperin
Okay, let's continue from where we stopped the last time and
talk a bit about the high level objectives that you will have in this specialization.
Your first objective should be to learn the core concepts
of machine learning such as types of machine learning,
notions of trading count, test sets,
all featuring categorization, and so on, and so forth.
Second, you want to understand
how specific machine learning algorithms such as logistic regression,
support vector machines, decision trees,
and solve work for financial problems.
Further you want to learn more about
neural networks maybe because you heard a lot about deep learning,
and you want to find out what neural networks and
deep learning are about and how they're used in finance.
Next you want to get that practical experience with using
Python Machine Learning libraries on financial problems including in particular,
search libraries as scikit-learn for classical machinery algorithms,
and library such as TensorFlow for neural models.
Finally, you want to know how you would be able to expand
your knowledge and learn more after you will complete this specialization.
All of the above will be our objectives here and we will pursue them
both in sequence by progressing through courses in this specialization.
Let me explain what we will do in each one of them.
Our first course is an introductory course
that I called a Guided Tour of Machine Learning in Finance.
This course will help you to grasp basic concepts of machine learning and
understand how in general machine learning in
finance differs from machine learning in the tech industry.
You will learn some simple machine learning
algorithms that we will use to analyze stock returns,
company fundamentals, bank reports,
and other financial data.
All with examples that present problems of practical interest.
I will show you Jupyter Notebooks that implement
these simple algorithms and then you will extend
them using different data in different algorithms.
In this first course,
we will be mostly using scikit-learn,
and a few other packages to implement our machine learning solutions.
Simultaneously, we will gently introduce TensorFlow,
a very powerful Python package for machine learning,
open source by Google about two years ago.
Though our main interest in TensorFlow is due to
its power of functionality for neural networks.
We will start with most simple examples of using
TensorFlow such as linear regression or logistic regression.
What comes to using neural networks themselves,
we will get a glimpse of how they work in the first course and build
our simple neural network for analysis of company earnings and bank statements.
During this course, we will have
weekly home assignments that will include programming assignments and optional reading.
The final course projects will be to apply
several supervised learning algorithms to bank reports data.
Don't worry now if you don't know what
the term supervised learning means, we will learn it soon.
Your final score for the course will be computed as a cumulative score
of your weekly homework assignments and your final course project.
Once you completed the first course,
the four work courses cover the main topics of machine learning paradigms.
For now, let me just name them for you.
They are called supervised learning,
unsupervised learning, and reinforcement learning.
Respectively, if each one of the follow up courses will
extend our first course in both the depth and the implications considered.
In our second course,
we will go into more details of supervised and unsupervised learning,
can get familiar with many key algorithms that belong in these types of machine learning.
We will be talking about various types of
regression and classification problems and see how
they can be solved using methods such as logistic regression,
support vector machines, or various types of decision trees.
Then, we will turn to unsupervised learning and talk about methods of
data analysis and data visualization such as
principal component analysis and its extensions,
clustering methods, and so on.
We will also look into neural networks and see how they can be used
to address the same types of tasks of supervised and unsupervised learning.
On a financial side,
we will be looking at many classical problems of
finance such as market and regime forecasts,
predictions of stock returns, portfolio optimization,
etc., and show how machine learning is applied for these tasks.
If you're only interested in
financial implications of supervised learning or unsupervised learning,
the first two courses should give you strong fundamentals
in understanding core concepts and main approaches in these fields.
So, that you would be able to go on and
use them in real life after completing these two courses.
My personal view though is that the most fascinating implications
of machine learning in finance come from reinforcement learning.
We will touch upon reinforcement learning in our first two courses,
and we will fully devote
our third course in this specialization to reinforcement learning,
and its financial implications.
In this course, we will talk about using of reinforcement learning to solve
such classical problems of finance as portfolio optimization,
optimal trading, and option pricing and risk management.
This third course is a bit more technically advanced than
the first two courses and we'll have a bit more math in it, but not by much.
The main difference of this course from other courses on reinforcement learning,
is that we will spend exactly zero time talking about
such traditional examples of reinforcement learning as Tic-Tac-Toe or robotics.
Instead we'll jump to financial problems right from the start and learn
classical methods of reinforcement learning such as the
famous Q- learning using financial problems.
This will be a hands-on course where you will be
implementing such algorithms and look how they work in practice.
In your course project for this course,
you will explore simple model for
market dynamics that is obtained using reinforcement learning.
After that, our final course,
we'll go deeper into topics of
reinforcement learning that we address in this third course.
Through the fourth course,
we will focus on more advanced topics,
but it will be less technical than the third one,
and will be structured as an overview of selected topics.
The first class of topics will focus on
fundamental concepts of finance such as market equilibrium, no arbitrage,
predictability, and so on,
and how reinforcement learning methods offer new views for such concepts.
We will also talk more about market modeling cues in reinforcement learning.
The second part of this last course will overview
applications of reinforcement learning to high frequency trading,
credit risk in peer-to-peer lending, and cryptocurrencies trading.
Finally, the last thing I need to discuss before we move on
with the first course in this specialization are prerequisites to this course,
textbooks, and course resources.
Let's talk about it in the next video.
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