Coursera
Paid Course
English
Certificate Available
17 weeks long, 5 hours a week
Overview
Class Central Tips
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.
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.
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.
Syllabus
Course 1: Guided Tour of Machine Learning in Finance
- Offered by New York University. This course aims at providing an introductory and broad overview of the field of ML with the focus on ... Enroll for free.
Course 2: Fundamentals of Machine Learning in Finance
- Offered by New York University. The course aims at helping students to be able to solve practical ML-amenable problems that they may ... Enroll for free.
Course 3: Reinforcement Learning in Finance
- Offered by New York University. This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use ... Enroll for free.
Course 4: Overview of Advanced Methods of Reinforcement Learning in Finance
- Offered by New York University. In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, ... Enroll for free.
- Offered by New York University. This course aims at providing an introductory and broad overview of the field of ML with the focus on ... Enroll for free.
Course 2: Fundamentals of Machine Learning in Finance
- Offered by New York University. The course aims at helping students to be able to solve practical ML-amenable problems that they may ... Enroll for free.
Course 3: Reinforcement Learning in Finance
- Offered by New York University. This course aims at introducing the fundamental concepts of Reinforcement Learning (RL), and develop use ... Enroll for free.
Course 4: Overview of Advanced Methods of Reinforcement Learning in Finance
- Offered by New York University. In the last course of our specialization, Overview of Advanced Methods of Reinforcement Learning in Finance, ... Enroll for free.
Taught by
Igor Halperin
