Fundamentals of Machine 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.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- Duration 18 hours
- Course by New York University
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Offered by
About
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.Modules
Machine Learning in Finance: Review
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2
Videos
2
Videos
- What is Machine Learning in Finance?
- Introduction to Fundamentals of Machine Learning in Finance
Lecture 1. Support Vector Machines
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4
Videos
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1
Readings
4
Videos
- Support Vector Machines, Part 1
- Support Vector Machines, Part 2
- SVM. The Kernel Trick
- Example: SVM for Prediction of Credit Spreads
1
Readings
- A. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, vol. 14, pp. 199-229, 2004
Lecture 2. Supervised Learning. Tree methods
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3
Videos
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2
Readings
3
Videos
- Tree Methods. CART Trees
- Tree Methods: Random Forests
- Tree Methods: Boosting
2
Readings
- 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
Module 1 Assessment
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1
Readings
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1
Lab
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1
Programming
- Random Forests And Decision Trees
1
Labs
- Random Forests And Decision Trees
1
Readings
- Jupyter Notebook FAQ
Core Concepts of Unsupervised Learning
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1
Videos
1
Videos
- Core Concepts of UL
Principal Component Analysis for Stock Returns
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2
Videos
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1
Readings
2
Videos
- PCA for Stock Returns, Part 1
- PCA for Stock Returns, Part 2
1
Readings
- C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 12.1
Dimension Reduction
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3
Videos
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1
Readings
3
Videos
- Dimension Reduction with PCA
- Dimension Reduction with tSNE
- Dimension Reduction with Autoencoders
1
Readings
- A. Geron, “Hands-On ML”, Chapters 8 & 15
Module 2 Assessment
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1
Readings
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1
Lab
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1
Programming
- Eigen Portfolio construction via PCA
1
Labs
- Eigen Portfolio construction via PCA
1
Readings
- Jupyter Notebook FAQ
Unsupervised Learning
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7
Videos
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2
Readings
7
Videos
- 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
2
Readings
- 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)
Module 3 Assessment
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1
Readings
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1
Lab
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1
Programming
- Data Visualization with t-SNE
1
Labs
- Data visualization with t-SNE
1
Readings
- Jupyter Notebook FAQ
Sequence Modeling
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6
Videos
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1
Readings
6
Videos
- SM. Latent Variables
- Sequence Modeling
- SM. Latent Variables for Sequences
- SM. State-Space Models
- SM. Hidden Markov Models
- Neural Architecture for Sequential Data
1
Readings
- C. Bishop, “Pattern Recognition and Machine Learning”, Chapter 13
Reinforcement Learning
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5
Videos
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1
Readings
5
Videos
- RL. Introduction
- RL. Core Ideas
- Markov Decision Process and RL
- RL. Bellman Equation
- RL and Inverse Reinforcement Learning
1
Readings
- S. Marsland, “Machine Learning: an Algorithmic Perspective” (Chapman & Hall 2009), Chapter 13
Course Project
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1
Readings
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1
Lab
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1
Programming
- Absorption Ratio via PCA
1
Labs
- Absorption Ratio via PCA
1
Readings
- Jupyter Notebook FAQ
Auto Summary
Unlock the power of machine learning in finance with this comprehensive course from Coursera, led by industry experts. Dive into supervised, unsupervised, and reinforcement learning techniques to solve real-world financial problems. Ideal for finance professionals, day traders, and students in related fields, this course requires basic Python and mathematical knowledge. Complete hands-on projects and gain practical skills over 1080 minutes. Available through Starter and Professional subscription options.
Igor Halperin