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Fundamentals of Machine Learning in Finance

Machine Learning and Reinforcement Learning in Finance,

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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Rating 2.9 based on 43 ratings
Length 5 weeks
Starts Nov 14 (4 weeks ago)
Cost $49
From New York University Tandon School of Engineering, New York University via Coursera
Instructor Igor Halperin
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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problem sets

The problem sets were also interesting, informative and introduced several useful api from sklearn, tensorflow.

With a little work these problem sets could (and probably should) be improved to match the quality of the lectures.

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Rating 2.9 based on 43 ratings
Length 5 weeks
Starts Nov 14 (4 weeks ago)
Cost $49
From New York University Tandon School of Engineering, New York University via Coursera
Instructor Igor Halperin
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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