Reinforce Your Career: Machine Learning in Finance. Extend your expertise of algorithms and tools needed to predict financial markets.

3.7

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Machine Learning and Reinforcement Learning in Finance 專項課程New York University

Compare ML for Finance with ML in Technology (image and speech recognition, robotics, etc.)

Describe linear regression and classification models and methods of their evaluation

Explain how Reinforcement Learning is used for stock trading

Become familiar with popular approaches to modeling market frictions and feedback effects for option trading.

Predictive ModellingFinancial EngineeringMachine LearningTensorflowReinforcement Learningoption pricing and risk managementsimple model for market dynamicsQ-learning using financial problemsoptimal tradingPortfolio Optimization

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on core paradigms and algorithms of machine learning (ML), with a particular focus on applications of ML to various practical problems in Finance.

The specialization aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include:

(1) mapping the problem on a general landscape of available ML methods,

(2) choosing particular ML approach(es) that would be most appropriate for resolving the problem, and

(3) successfully implementing a solution, and assessing its performance.

The specialization 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.

The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.

The specialization is essentially in ML where all examples, home assignments and course projects deal with various problems in Finance (such as stock trading, asset management, and banking applications), and the choice of topics is respectively driven by a focus on ML methods that are used by practitioners in Finance. The specialization is meant to prepare the students to work on complex machine learning projects in finance that often require both a broad understanding of the whole field of ML, and understanding of appropriateness of different methods available in a particular sub-field of ML (for example, Unsupervised Learning) for addressing practical problems they might encounter in their work.

學生職業成果

50%

完成此 專項課程 後開始了新的職業。

可分享的證書

完成後獲得證書

100% 在線課程

立即開始，按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

Basic math including calculus and linear algebra, basic probability theory and statistics, and programming skills in Python.

完成時間大約為4 個月

建議 5 小時/週

英語（English）

字幕：英語（English）, 法語（French）

學生職業成果

50%

完成此 專項課程 後開始了新的職業。

可分享的證書

完成後獲得證書

100% 在線課程

立即開始，按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

完成時間大約為4 個月

建議 5 小時/週

英語（English）

字幕：英語（English）, 法語（French）

3.8

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521 個評分

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162 條評論

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.

3.8

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260 個評分

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53 條評論

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.

3.5

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94 個評分

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24 條評論

This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use cases for applications of RL for option valuation, trading, and asset management.

By the end of this course, students will be able to - Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management. - Practice on valuable examples such as famous Q-learning using financial problems. - Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project. Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.

3.8

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In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, we will take a deeper look into topics discussed in our third course, Reinforcement Learning in Finance.

In particular, we will talk about links between Reinforcement Learning, option pricing and physics, implications of Inverse Reinforcement Learning for modeling market impact and price dynamics, and perception-action cycles in Reinforcement Learning. Finally, we will overview trending and potential applications of Reinforcement Learning for high-frequency trading, cryptocurrencies, peer-to-peer lending, and more. After taking this course, students will be able to - explain fundamental concepts of finance such as market equilibrium, no arbitrage, predictability, - discuss market modeling, - Apply the methods of Reinforcement Learning to high-frequency trading, credit risk peer-to-peer lending, and cryptocurrencies trading.

Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

Can I take the course for free?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

What background knowledge is necessary?

Prerequisites for the specialization are basic math including calculus and linear algebra, basic probability theory and statistics, and some programming skills in Python. For students that are not familiar with Python and IPython / Jupyter notebooks, reference to tutorials are provided as a part of further reading.

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