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New York University (NYU)

Guided Tour of Machine Learning in Finance

New York University (NYU) via Coursera

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Overview

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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.

Syllabus

  • Artificial Intelligence & Machine Learning
  • Mathematical Foundations of Machine Learning
  • Introduction to Supervised Learning
  • Supervised Learning in Finance

Taught by

Igor Halperin

Reviews

1.1 rating, based on 7 Class Central reviews

3.8 rating at Coursera based on 630 ratings

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  • Profile image for Luiz Cunha
    Luiz Cunha

    Luiz Cunha completed this course, spending 4 hours a week on it and found the course difficulty to be easy.

    First very much interested by the topic, where I have some professional knowledge. Unfortunately this MOOC is subject of strong disappointment: some videos raise some hope about content, which is always further down the road disappointed. And the very...
  • Steven Oshry

    Steven Oshry is taking this course right now, spending 5 hours a week on it and found the course difficulty to be medium.

    I would give this class zero stars if I could. It is a great topic and I had high expectations. The assignments are poorly worded, instructions are vague and that is putting it mildly. The material required to complete the assignments is mostly not covered in the lectures. I can't believe NYU gives its name to this jumbled mess. Buyer Beware!
  • Anonymous

    Anonymous is taking this course right now.

    Submission of assignments were extremely problematic. Despite many of the students agreeing on the forum that we were working on it correctly. Worst is grading always return error but nothing is done on the instructor side to resolve them. Instructors were not very responsive.
  • Anonymous

    Anonymous completed this course.

    The subject is great but the instructor doesn't give you any detailed clue for doing the assignment. He seems that he is reading the speech from a monitor so there is no difference between him and a text to speech machine.
  • Profile image for Joakim Kosmo
    Joakim Kosmo

    Joakim Kosmo is taking this course right now.

    Grading has been down for two weeks now. No student have been able to submit assignments. The forums is full of complaints, non of the teaching staff is responding.
  • Nauman Ahmad

    Nauman Ahmad is taking this course right now.

    Disappointed. Not what was expected. Assignments were a big headache too. The lecturer wasn't good either. Dropped the course because it didn't meet the initial expectations.
  • Anonymous

    Anonymous completed this course.

    I am not going to be as critical as others on this website and give two stars to this course. This is not a bad course, but the title should say machine learning theory with tensorflow. This course has nothing to do with the finance. 90% of the course is explanation of ml algorithms, so pure maths.
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