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Guided Tour of Machine Learning in Finance に戻る

New York University による Guided Tour of Machine Learning in Finance の受講者のレビューおよびフィードバック



This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course 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 Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course....




Very useful course. Personally, I think that there should have been more focus on the implementation of tensorflow and neural network codes. Overall the course is well structured and very clear.



Introduction of ML for Financial application with combination of Scikit learn, Statsmodels and Tensorflow with neuralnets made this class very interesting. Learned and Enjoyed lot.


Guided Tour of Machine Learning in Finance: 76 - 100 / 193 レビュー

by gareth o


Lectures are very good and the use of financial examples really brings the subject alive. However the final projects are not very closely linked to the material taught, it's possible to pass if you ignore the new material. It would also be nice to update the tensorflow code from 1.0 to 2.0 as it would make things much easier to debug.

by Pedro H


Potentially great course with bridges technology (machine learning methods) and application (finance), but as for now it is really rough around the edges. Still needs to improve in terms of video lectures, resources and assignments; but once polished it could be a great course/specialization.

by Jose G H C


Um curso um que demanda um pouco mais que o usual, partindo desde o princípio de um ritmo rápido, com tarefas contendo explicações de somente o estritamente necessário. Entretanto, com uma temática muito interessante, e utilizando de várias técnicas.

by Philip T


Assignments are extremely difficult because the instructions are not clear. I understand that the act of working through the assignments is how you learn the material, however, this goes beyond that. It felt like a battle.

by Fred U


Great lectures. Homework is not trivial: it requires web searches and significantly more perseverance than, say, Andrew Ng's courses. Only 4 stars because I didn't see any recent signs of active support in the Forums.

by Marina Z


The course seems a bit of date (tensorflow) and 'lazy' -- assignments are sloppy, not related to the content of lectures sometimes, sometimes just replay of things form reading material... Promised more than delivered.

by Nayan a


Lectures are mostly short review of the topic. So you should know topics beforehand or supplement it with readings. Problems are great, you cannot solve it unless you understand the concept properly, so that good point.

by Bozanian K


Very interesting course. Covers the main algorithms of supervised machine learning and their applications to the world of finance. The one and only down is that programming session are a little hard to understand

by Jochen G


Deep introduction into machine learning in finance. A bit outdated API-usage (Tensorflow 1), but nevertheless a great introduction for those who want to understand how the NN are processing the data.

by Mihails S


Despite all the problems with the assignments and the grader this course provides really good overview ML tools and their application to finance. It's definitely worth the effort

by Zhiming X


The course content is a mix of theory and practical stuff. One star off is due to the poor quality of programming assignment, i.e., unclear instructions and explanations.

by Maksim G


Good material but assignments explanation were too sparse and even expectation of material not covered in videos or readings (example is Tobit regression in week 4).

by Aydar A


To much math in lectures, assignments are not coherent and complicated, im not sure that i need tensorflow from scratch to work with finance(Keras fits better)

by Hongsun K


Great general overview of machine learning. I think the course can be re-organized to incorporate some of the theory and some coding tips as well, however.

by Manimaran P


The Lectures and given readings are very useful and it is required to read them to complete the assignments which will otherwise be difficult

by Chad W L


This will be a 5 star course when all of the technical issues are resolved. More timely feedback from the staff is desirable as well.

by Ishrit T


A more detailed introduction and guide to python for machine learning would have made this course one of the best out there

by Julien T


Very interesting content well delivered, the programming assignments could benefit from a little more guidance IMHO.

by Kelly Y


Great overview. Please provide more code examples as homework require a lot more than what the class covers!

by Songjie H


Homework is not always consistent with what's covered in class. The recommended readings are very helpful.

by Takayuki K


One of assignments was hard. Explanation by lecturer was very easy to understand and appropriate long.

by Amalka W


It would be great the background theory of related concept are explained in optional videos.

by Martin K


It's a good course. There are some missing explanations in the programming exercises.

by Zoraiz A


Later assignmnets were difficult but lecture material is interesting and well taught.

by Rafael D d D


Very good review and selected topics, although I would deep more on tensorflow use