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.
This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization
Offered By
Machine Learning and Reinforcement Learning in Finance SpecializationNew York UniversityAbout this Course
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Course 4 of 4 in the
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Advanced Level
Approx. 14 hours to complete
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 14 hours to complete
English
Subtitles: English
Offered by
New York University
Tandon offers comprehensive courses in engineering, applied science and technology. Each course is rooted in a tradition of invention and entrepreneurship.
Syllabus - What you will learn from this course
4 hours to complete
Black-Scholes-Merton model, Physics and Reinforcement Learning
4 hours to complete
13 videos (Total 103 min)
13 videos
Specialization Prerequisites7m
Interview with Rossen Roussev14m
Reinforcement Learning and Ptolemy's Epicycles5m
PDEs in Physics and Finance5m
Competitive Market Equilibrium Models in Finance5m
I Certainly Hope You Are Wrong, Herr Professor!7m
Risk as a Science of Fluctuation3m
Markets and the Heat Death of the Universe3m
Option Trading and RL14m
Liquidity9m
Modeling Market Frictions9m
Modeling Feedback Frictions10m
1 practice exercise
Assignment 12h
3 hours to complete
Reinforcement Learning for Optimal Trading and Market Modeling
3 hours to complete
8 videos (Total 73 min)
8 videos
Invisible Hand5m
GBM and Its Problems9m
The GBM Model: An Unbounded Growth Without Defaults9m
Dynamics with Saturation: The Verhulst Model7m
The Singularity is Near9m
What are Defaults?11m
Quantum Equilibrium-Disequilibrium11m
1 practice exercise
Assignment 22h
3 hours to complete
Perception - Beyond Reinforcement Learning
3 hours to complete
8 videos (Total 60 min)
8 videos
Market Dynamics and IRL5m
Diffusion in a Potential: The Langevin Equation8m
Classical Dynamics7m
Potential Minima and Newton's Law4m
Classical Dynamics: the Lagrangian and the Hamiltonian7m
Langevin Equation and Fokker-Planck Equations9m
The Fokker-Planck Equation and Quantum Mechanics12m
1 practice exercise
Assignment 32h
4 hours to complete
Other Applications of Reinforcement Learning: P-2-P Lending, Cryptocurrency, etc.
4 hours to complete
9 videos (Total 79 min)
9 videos
Electronic Markets and LOB9m
Trades, Quotes and Order Flow7m
Limit Order Book8m
LOB Modeling8m
LOB Statistical Modeling10m
LOB Modeling with ML and RL9m
Other Applications of RL7m
The Value of Universatility15m
Reviews
TOP REVIEWS FROM OVERVIEW OF ADVANCED METHODS OF REINFORCEMENT LEARNING IN FINANCE
by DDDec 11, 2019
Great refreshment on Stochastic calculus and overall rewind of the specialization!
by ITDec 8, 2019
It was very difficult to get the peer-graded assignments graded.
About the 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 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.
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