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.
IT
It was very difficult to get the peer-graded assignments graded.
DY
Great refreshment on Stochastic calculus and overall rewind of the specialization!
显示：16/16
Assessments are once again out of touch with the materials that have been presented and do not reflect any practical uses you may need to work on in the industry. Skip this certificate until fixed.
Contents of Week1 and Week4 are really useful, as the instructor recommended several academic papers on relevant topics. However the instructor failed to expand them, at least will be helpful to outline the basic ideas of each paper. The instructor only mentioned the authors' names and paper title. It's a pity.
However, week 2 and week 3 are totally useless in understanding finance and reinforcement learning. It's just a pile of formulas from physics, not interesting or pertinent to course topic at all. Moreover there is a strange signal term in the drift of stochastic process. I don't think anyone in industry is ever using this less-known dynamic to pricing or trading.
It's definitely better that Week2 and Week3 could be removed completely and be replaced by expansions of the academic papers that the instructor recommended.
No real follow up by the team, and the assignments have nothing to do with the classes.
When I got down to the course 4, I completely collapsed as it almost had nothing to do with "reinforcement learning". The lectures almost have nothing to do with the core of this specializaiton "reinforcement learning". The project has nothing to do with Reinforcement learning, it is to use MLE to estimate parameters of a model proposed in the paper of the instructor.
I am really pissed off by taking this specialization. I thought I would learn something, but it turened out a complete waste of my one-month time.
BTW, if you wanted to learn quantum mechanics, this is the course.
Never have wasted my time on anything as useless as this one! If I wanted to go read the book to learn and take the exam I wouldn't need you. Just don't take this course Or any of the courses on this specialization.
It was very difficult to get the peer-graded assignments graded.
Interesting deep dive into a RL application in Finance at forefront of research, however be prepared for challenging project assignments with limited support or guidance. Not for the fainthearted.
The "Overview of Advanced Methods of Reinforcement Learning in Finance" course covers advanced reinforcement learning (RL) concepts applied to finance scenarios, including option pricing, market modeling, optimal trade, high-frequency trading, and cryptocurrency trading. However, the course's broad overview may not provide an in-depth technical understanding of each topic, leaving those seeking more information wanting more profound knowledge. Anyone interested can find a complete review of the course at: https://www.linkedin.com/posts/dsolis_machinelearning-quantitativefinance-mathematicalfinance-activity-7172615209851822080-UE26
Great course. You require lot of patience to complete the course. Uses lot of unnecessary history, symbols and equations to explain simple concepts. Overall it is a good overview of the big picture of RL in finance provided if u can withstand the assault of excessive mindless symbols and equations.
I really recommend this course,some courses your learn just tell you what to do,but when you finish this one,you will want to dig deeper.wonderful!
Great refreshment on Stochastic calculus and overall rewind of the specialization!
Excellent course!!!
Great course.
Thanks
Mostly bad, just like the other courses. Mostly just reading formulas out loud. It's a physicist who's trying to teach finance and failing miserably. I'm sure he's been able to make money in the private sector for very large companies, but the outcomes of this course was completely missed by the objectives.
It's more finance-oriented than to applied ML