This course is part of the Machine Learning and Reinforcement Learning in Finance Specialization

3.8
stars
321 ratings

Igor Halperin

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Offered By

Machine Learning and Reinforcement Learning in Finance SpecializationNew York University

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 2 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Intermediate Level

Approx. 18 hours to complete

English

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 4 in the

Machine Learning and Reinforcement Learning in Finance Specialization

Intermediate Level

Approx. 18 hours to complete

English

Instructor

Instructor rating

3.95/5 (25 Ratings)

Igor Halperin

45,935 Learners

4 Courses

Offered by

New York University

Syllabus - What you will learn from this course

Week1

Week 1

5 hours to complete

Fundamentals of Supervised Learning in Finance

5 hours to complete

9 videos (Total 71 min), 4 readings, 1 quiz

9 videos

What is Machine Learning in Finance?5mIntroduction to Fundamentals of Machine Learning in Finance4mSupport Vector Machines, Part 18mSupport Vector Machines, Part 27mSVM. The Kernel Trick8mExample: SVM for Prediction of Credit Spreads9mTree Methods. CART Trees9mTree Methods: Random Forests8mTree Methods: Boosting9m

4 readings

A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415mA. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730mK. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415mJupyter Notebook FAQ10m

Week2

Week 2

4 hours to complete

Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction

4 hours to complete

6 videos (Total 54 min), 3 readings, 1 quiz

Week3

Week 3

4 hours to complete

Data Visualization & Clustering

4 hours to complete

7 videos (Total 50 min), 3 readings, 1 quiz

7 videos

UL. Clustering Algorithms4mUL. K-clustering8mUL. K-means Neural Algorithm7mUL. Hierarchical Clustering Algorithms10mUL. Clustering and Estimation of Equity Correlation Matrix5mUL. Minimum Spanning Trees, Kruskal Algorithm6mUL. Probabilistic Clustering6m

3 readings

C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930mG. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15mJupyter Notebook FAQ10m

Week4

Week 4

5 hours to complete

Sequence Modeling and Reinforcement Learning

5 hours to complete

11 videos (Total 101 min), 3 readings, 1 quiz

11 videos

SM. Latent Variables7mSequence Modeling10mSM. Latent Variables for Sequences8mSM. State-Space Models9mSM. Hidden Markov Models9mNeural Architecture for Sequential Data12mRL. Introduction8mRL. Core Ideas7mMarkov Decision Process and RL8mRL. Bellman Equation6mRL and Inverse Reinforcement Learning11m

3 readings

C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310mS. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315mJupyter Notebook FAQ10m

Reviews

3.8

74 reviews

5 stars
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3 stars
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2 stars
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TOP REVIEWS FROM FUNDAMENTALS OF MACHINE LEARNING IN FINANCE

by SDSep 18, 2019

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

by ATAug 9, 2019

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

by JJDec 24, 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.

by ATSep 2, 2019

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

View all reviews

About the Machine Learning and Reinforcement Learning in Finance Specialization

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) successfully implementing a solution, and assessing its performance. The specialization 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. The modules can also be taken individually to improve relevant skills in a particular area of applications of ML to finance.

Machine Learning and Reinforcement Learning in Finance

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Fundamentals of Machine Learning in Finance

About this Course