Description
Machine Learning and Reinforcement Learning in Finance Specialization
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Descriptions
Machine Learning and Reinforcement Learning in Finance Specialization, 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 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.
What you’ll learn
- 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.
Who this course is for
- 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,
Requirements
- Basic math including calculus and linear algebra, basic probability theory and statistics, and programming skills in Python.
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