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Fundamentals of Machine Learning in Finance, Université de New York, Tandon School of Engineering

3.6
109 notes
24 avis

À propos de ce cours

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance. A learner with some or no previous knowledge of Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance. Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy. 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....
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23 avis

par Umendra Chauhan

Feb 02, 2019

This could have been the real deal with so many fascinating topics to learn here, but unfortunately, this specialization is setting new low standards in each assignments. The grader does not work, sometime we are asked to produce wrong results (as oppose to the research material). It is very frustrating!

Good reading assignments.

They need better and more qualified support staff.

par Daniel Fudge

Jan 13, 2019

Content is good but assignments are buggy.

par Pramanshu Rajput

Jan 08, 2019

Content and programming assignments are not much correlated. Lots of kernel problems while submitting assignments and late reply by staff.

par Luis Alberto Alaniz Castillo

Jan 07, 2019

Excellent course.

I only wish to have had programming assignment with RNN and Hidden Markov Models instead of three assignments on PCA. Although they highlighted a interesting application in finance.

par Jacques Joubert

Dec 25, 2018

So far so good. The lecturer refers to projects of which some weren't covered in this course. So a little confusing. Takes lots of googling to finish this course.

par Pavel Konovalov

Nov 28, 2018

Very informative

par Andreas Atle

Nov 21, 2018

Completely horrible labs.

And no response on the forums, errors in the labs remains for several months.

This is not acceptable, the course should be removed from Coursera!

par 刘晶

Nov 06, 2018

It's excellent and incomparable course!

par Amalka Withana

Nov 01, 2018

If assignment are clear this course would be a great one. So I would like to suggest that explain more details about assignment and some guide lines

par Philip Tabak

Oct 25, 2018

Many technical issues with assignments. Additionally, assignment instructions are often poor or insufficient.