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.
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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 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.
What's inside
Four courses
Guided Tour of Machine Learning in Finance
Guided Tour of Machine Learning in Finance
Fundamentals of Machine Learning in Finance
Fundamentals of Machine Learning in Finance
Reinforcement Learning in Finance
Reinforcement Learning in Finance
Overview of Advanced Methods of Reinforcement Learning in Finance
Overview of Advanced Methods of Reinforcement Learning in Finance
Learning objectives
- 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.
- 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.
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