About this Course

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Learner Career Outcomes

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Shareable Certificate
Earn a Certificate upon completion
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Flexible deadlines
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Intermediate Level
Approx. 18 hours to complete
English
Subtitles: English

Learner Career Outcomes

20%

started a new career after completing these courses

18%

got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 18 hours to complete
English
Subtitles: English

Instructor

Offered by

New York University logo

New York University

Syllabus - What you will learn from this course

Week
1

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
Introduction to Fundamentals of Machine Learning in Finance4m
Support Vector Machines, Part 18m
Support Vector Machines, Part 27m
SVM. The Kernel Trick8m
Example: SVM for Prediction of Credit Spreads9m
Tree Methods. CART Trees9m
Tree Methods: Random Forests8m
Tree Methods: Boosting9m
4 readings
A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 200415m
A. Geron, “Hands-On Machine Learning with Scikit-Learn and TensorFlow”, Chapters 6 & 730m
K. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2009, Chapter 16.415m
Jupyter Notebook FAQ10m
Week
2

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
6 videos
PCA for Stock Returns, Part 14m
PCA for Stock Returns, Part 29m
Dimension Reduction with PCA9m
Dimension Reduction with tSNE11m
Dimension Reduction with Autoencoders9m
3 readings
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.115m
A. Geron, “Hands-On ML”, Chapters 8 & 1530m
Jupyter Notebook FAQ10m
Week
3

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. K-clustering8m
UL. K-means Neural Algorithm7m
UL. Hierarchical Clustering Algorithms10m
UL. Clustering and Estimation of Equity Correlation Matrix5m
UL. Minimum Spanning Trees, Kruskal Algorithm6m
UL. Probabilistic Clustering6m
3 readings
C. Bishop, “Pattern Recognition and Machine Learning”, Clustering and EM: Chapter 930m
G. Bonanno et. al. “Networks of equities in financial markets”, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)15m
Jupyter Notebook FAQ10m
Week
4

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
Sequence Modeling10m
SM. Latent Variables for Sequences8m
SM. State-Space Models9m
SM. Hidden Markov Models9m
Neural Architecture for Sequential Data12m
RL. Introduction8m
RL. Core Ideas7m
Markov Decision Process and RL8m
RL. Bellman Equation6m
RL and Inverse Reinforcement Learning11m
3 readings
C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 1310m
S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 1315m
Jupyter Notebook FAQ10m

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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

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