Coursera近期新推了一个金融和机器学习的专项课程系列：Machine Learning and Reinforcement Learning in Finance Specialization（金融中的机器学习和强化学习），看起来很有意思。
课程链接：http://coursegraph.com/coursera-specializations-machine-learning-reinforcement-finance
这个专项课程的主要目标是为金融相关的机器学习核心范式和算法奠定坚实的基础而提供必要的知识和实战技能，特别关注机器学习在金融投资中不同的实际问题中的应用。
该系列旨在帮助学生解决他们在现实生活中可能遇到的实际的机器学习问题，包括：
（1）将问题映射到可用的机器学习方法的泛化场景，
（2）选择最适合解决问题的特定机器学习方法，以及
（3）成功实施解决方案，并评估其性能。
该专业课程面向三类学生设计：
· 在银行，资产管理公司或对冲基金等金融机构工作的从业人员
· 对将机器学习应用于日内交易感兴趣的个人
· 目前正在攻读金融学，统计学，计算机科学，数学，物理学，工程学或其他相关学科的学位的全日制学生，这些学生希望了解机器学习在金融领域的实际应用。
这个专项课程由纽约大学推出，包含4门子课程：
Guided Tour of Machine Learning in Finance（金融中的机器学习导览）
http://coursegraph.com/coursera-guided-tour-machine-learning-finance
本课程的目的是提供一个关于机器学习领域的介绍和广泛的概括，重点是机器学习在金融中的应用。目标是让学生了解机器学习是什么，机器学习面向的是什么以及它可以应用于多少不同的金融问题。
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.
Fundamentals of Machine Learning in Finance(金融中的机器学习基础知识)
http://coursegraph.com/coursera-fundamentals-machine-learning-in-finance
该课程旨在帮助学生解决他们在现实生活中可能遇到的实际机器学习问题，包括：（1）理解所面临的问题并且能够找到合适的机器学习方法大致框架，（2）知道哪个特定的机器学习方法最适合解决该问题，（3）拥有成功实施解决方案并评估其性能的能力。具有一些或不具备机器学习知识的学习者将了解有监督学习和无监督学习，以及强化学习的主要算法，并且将能够使用机器学习开源Python包来设计，测试和实现金融中的机器学习算法。金融机器学习的基础知识将提供更深入的有监督，无监督和强化学习内容，课程将以一个使用无监督学习来实现简单投资组合交易策略的项目作为结束。
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.
Reinforcement Learning in Finance（金融中的强化学习)
http://coursegraph.com/coursera-reinforcement-learning-in-finance
本课程旨在介绍强化学习的基本概念，并开发用于期权评估，交易和资产管理的强化学习应用的用例。先修课程是“金融中的机器学习导览”和“金融中的机器学习基础”课程。
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. Prerequisites are the courses “Guided Tour of Machine Learning in Finance” and “Fundamentals of Machine Learning in Finance”.
Overview of Advanced Methods of Reinforcement Learning in Finance（强化学习在金融中的高级方法概述）
http://coursegraph.com/coursera-advanced-methods-reinforcement-learning-finance
在这个系列的最后一个课程“强化学习在金融中的高级方法概述”中，将深入研究第三门课程“金融中的强化学习”中讨论的主题。特别是将讨论强化学习，期权定价和物理学之间的联系，逆向强化学习对建模市场影响和价格动态的影响，以及强化学习中的感知行动周期。最后，将概述强化学习在高频交易，加密货币，点对点借贷等方面的趋势和潜在应用。
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.
