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Reinforcement Learning in Finance

Machine Learning and 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. By the end of this course, students will be able to - Use reinforcement learning to solve classical problems of Finance such as portfolio optimization, optimal trading, and option pricing and risk management. - Practice on valuable examples such as famous Q-learning using financial problems. - Apply their knowledge acquired in the course to a simple model for market dynamics that is obtained using reinforcement learning as the course project. Prerequisites are the courses "Guided Tour of Machine Learning in Finance" and "Fundamentals of Machine Learning in Finance". Students are expected to know the lognormal process and how it can be simulated. Knowledge of option pricing is not assumed but desirable.

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Rating 2.7 based on 18 ratings
Length 5 weeks
Starts Nov 14 (4 weeks ago)
Cost $49
From New York University Tandon School of Engineering, New York University via Coursera
Instructor Igor Halperin
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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What people are saying

complicated by inconsistent use

The problem is further complicated by inconsistent use of notation and excessive amount of details which are not needed to put the concepts into code.

stock trading..to benefit fully

Excellent overview of reinforcement learning with applications to option pricing and stock trading..To benefit fully from this course, a good command of python and various libraries for machine learning/data science is essential...

analogy without being aware

The instructor has been overly obsessed in his physics analogy without being aware what he actually needs to teach.

consider other options instead

If someone wants to learn machine learning in finance with efficiency and practicality, he or she should consider other options instead of this specialization/course.

finance knowledge than advertised

Tests at the end of the videos cut what Igor is saying and they are often about the following video.The assessments have no interaction with what we are supposed to learn, the 10-people staff is never online and never answers any message, and you need far more finance knowledge than advertised.

professor takes several weeks

The professor takes several weeks attempting to relate between the two in order to provide a mathematical framework to price options through reinforcement learning, but fails to present this information in a way that is straight forward to put into practice.

received mixed reviews because

I think it's received mixed reviews because I can imagine it would be a lot to take in if you try and complete the course in the allocated time.

recommended adjusting your expectations

To get something out of this course I would recommended adjusting your expectations (in terms of time scales) and taking you're time (I've been doing it on and off for a couple of months) to complete the course.

black scholes equations

The material covered in the course is mostly focused around the mathematics of the Bellman and Black Scholes equations.

following video.the assessments

his physics analogy

just feels like

it just feels like the course was rushed to production and they let the students debug it.

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Learning Experience Designer $67k

Audio Engineer & Live Sound Reinforcement $68k

Individualized Learning Specialist $79k

Enterprise Learning Consultant $98k

Global Learning Specialist $106k

Consulting Learning Specialist $108k

Learning and Development Consultant 3 $112k

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Rating 2.7 based on 18 ratings
Length 5 weeks
Starts Nov 14 (4 weeks ago)
Cost $49
From New York University Tandon School of Engineering, New York University via Coursera
Instructor Igor Halperin
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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