NYU - Fundamentals of Machine Learning in Finance
- Offered byCoursera
Fundamentals of Machine Learning in Finance at Coursera Overview
Duration | 18 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Fundamentals of Machine Learning in Finance at Coursera Highlights
- 20% started a new career after completing these courses.
- 17% got a tangible career benefit from this course.
- Earn a shareable certificate upon completion.
Fundamentals of Machine Learning in Finance at Coursera Course details
- 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.
Fundamentals of Machine Learning in Finance at Coursera Curriculum
Fundamentals of Supervised Learning in Finance
What is Machine Learning in Finance?
Introduction to Fundamentals of Machine Learning in Finance
Support Vector Machines, Part 1
Support Vector Machines, Part 2
SVM. The Kernel Trick
Example: SVM for Prediction of Credit Spreads
Tree Methods. CART Trees
Tree Methods: Random Forests
Tree Methods: Boosting
A. Smola and B. Scholkopf, ?A Tutorial on Support Vector Regression?, Statistics and Computing, vol. 14, pp. 199-229, 2004
A. Geron, ?Hands-On Machine Learning with Scikit-Learn and TensorFlow?, Chapters 6 & 7
K. Murphy, ?Machine Learning: A Probabilistic Perspective?, MIT Press, 2009, Chapter 16.4
Jupyter Notebook FAQ
Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
Core Concepts of UL
PCA for Stock Returns, Part 1
PCA for Stock Returns, Part 2
Dimension Reduction with PCA
Dimension Reduction with tSNE
Dimension Reduction with Autoencoders
C. Bishop, ?Pattern Recognition and Machine Learning?, Chapter 12.1
A. Geron, ?Hands-On ML?, Chapters 8 & 15
Jupyter Notebook FAQ
Data Visualization & Clustering
UL. Clustering Algorithms
UL. K-clustering
UL. K-means Neural Algorithm
UL. Hierarchical Clustering Algorithms
UL. Clustering and Estimation of Equity Correlation Matrix
UL. Minimum Spanning Trees, Kruskal Algorithm
UL. Probabilistic Clustering
C. Bishop, ?Pattern Recognition and Machine Learning?, Clustering and EM: Chapter 9
G. Bonanno et. al. ?Networks of equities in financial markets?, The European Physical Journal B, vol. 38, issue 2, pp. 363-371 (2004)
Jupyter Notebook FAQ
Sequence Modeling and Reinforcement Learning
SM. Latent Variables
Sequence Modeling
SM. Latent Variables for Sequences
SM. State-Space Models
SM. Hidden Markov Models
Neural Architecture for Sequential Data
RL. Introduction
RL. Core Ideas
Markov Decision Process and RL
RL. Bellman Equation
RL and Inverse Reinforcement Learning
C. Bishop, ?Pattern Recognition and Machine Learning?, Chapter 13
S. Marsland, ?Machine Learning: an Algorithmic Perspective? (Chapman & Hall 2009), Chapter 13
Jupyter Notebook FAQ
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