**9,501**人已註冊

### 您將學到的內容有

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.

### 您將獲得的技能

## 關於此 專項課程

## 應用的學習項目

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.

#### 可分享的證書

#### 100% 在線課程

#### 靈活的計劃

#### 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5 個月

#### 英語（English）

### 專項課程的運作方式

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### 實踐項目

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### 此專項課程包含 4 門課程

### Guided Tour of Machine Learning in Finance

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.

### Fundamentals of Machine 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.

### Reinforcement Learning in Finance

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.

### Overview of Advanced Methods of Reinforcement Learning in Finance

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.

### 提供方

#### 纽约大学坦登工程学院

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

### 審閱

#### 3.7

##### 來自MACHINE LEARNING AND REINFORCEMENT LEARNING IN FINANCE的熱門評論

Great refreshment on Stochastic calculus and overall rewind of the specialization!

Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.

More or less this course is good and interesting. However, homework assignments were awful. It's unclear and it's very hard to understand what is asked and how it would be graded.

Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort

The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.

Excellent course. The peer reviewed evaluation is very interisting and it is definitely worth the time to do it in detail but does not take two hours with luck a week.

Good material but assignments explanation were too sparse and even expectation of material not covered in videos or readings (example is Tobit regression in week 4).

So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.

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此专项课程不提供大学学分，但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校了解详情。

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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