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Fundamentals of Machine Learning in Finance

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) succes

2.9

33 reviews

Created by: Igor Halperin

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Overall Score : 58 / 100

Course Description

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 fundsIndividuals interested in applications of ML for personal day tradingCurrent 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.

Time: 16h

Instructor Details

Igor Halperin

2.9 Rating
33 Reviews

Igor Halperin is Research Professor of Financial Machine Learning at NYU Tandon School of Engineering. His research focuses on using methods of Reinforcement Learning, Information Theory, neuroscience and physics for financial problems such as portfolio optimization, dynamic risk management, and inference of sequential decision-making processes of financial agents. Igor has an extensive industrial experience in statistical and financial modeling, in particular in the areas of option pricing, credit portfolio risk modeling, portfolio optimization, and operational risk modeling. Prior to joining NYU Tandon, Igor was an Executive Director of Quantitative Research at JPMorgan, and before that he worked as a quantitative researcher at Bloomberg LP. Igor has published numerous articles in finance and physics journals, and is a frequent speaker at financial conferences. He has also co-authored the book "Credit Risk Frontiers" published by Bloomberg LP. Igor has a Ph.D. in theoretical high energy physics from Tel Aviv University, and a M.Sc. in nuclear physics from St. Petersburg State Technical University. He advices a several fintech and data science start-ups and risk management firms.

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2.9

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By Daria on 26-Oct-19

Great overview of main ML concepts with examples applicable to Finance. Even though some people might argue, that the videos don't provide a clear guide path to the assignments, I believe the course provides a simple explanation and great book references! Also, I supplemented my study with courses @DataCamp and other open sources - and it was quite beneficial as well. Thank you, Igor Halperin, & a team!

By Siyu D on 19-Sep-19

This is a great course, I strongly recommend. However, the assignments take a while to finish.

By Arditto T on 3-Sep-19

Great course which covers both theories as well as practical skills in the real implementations in the financial world.

By Angelo J I T on 10-Aug-19

Furthered my understanding of how probabilistic models are connected to Machine Learning models. Very happy with the content in this course.

By Omar E O F on 1-Jul-19

Not enough support from the staff. The assignments are strange, some don't relate to the lectures, some are hard to identify what kind of answer is being expected from us. Discussion forums are a joke, I've managed to go through because of some good souls like Kurt Woschnagg. Great lectures though, content is very nice, but not enough visualizations of the math presented.

By Aydar A on 28-Jun-19

Good course with relevant topics, but assignments are not clear sometimes, lack of support with them.

By Nicolas M on 1-Apr-19

good overview of methods but project part was frustrating due to slow Jupyter servers which blocked progress. Overall still positive as course content is unique.

By Ronald B on 31-Mar-19

Assignments are poorly designed. Staff is unresponsive. IThe same appened with the first course of the specialization.

By Teemu P on 3-Mar-19

Do not attempt this course unless you are extremely experienced in the topic and python already.

By Lingzi on 24-Feb-19

the course content is okay. but the coding exam really needs improvement.

By Umendra C on 2-Feb-19

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.

By Daniel F on 13-Jan-19