Fundamentals of Machine Learning in Finance
Master machine learning algorithms and their financial applications, from supervised learning to reinforcement learning, with hands-on Python implementation.
Master machine learning algorithms and their financial applications, from supervised learning to reinforcement learning, with hands-on Python implementation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Machine Learning and Reinforcement Learning in Finance Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
3.7
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Implement supervised learning algorithms for financial prediction
Apply dimensionality reduction techniques to financial data
Develop clustering methods for market analysis
Master sequence modeling and reinforcement learning
Create practical machine learning solutions for trading strategies
Skills you'll gain
This course includes:
4.5 Hours PreRecorded video
4 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores the fundamentals of machine learning applications in finance, covering both theoretical concepts and practical implementation. Students learn supervised learning techniques like Support Vector Machines and Random Forests, unsupervised learning methods including PCA and clustering, and an introduction to reinforcement learning. The curriculum emphasizes hands-on experience through Python programming assignments and focuses on real-world financial applications such as credit spread prediction, portfolio construction, and market analysis.
Fundamentals of Supervised Learning in Finance
Module 1 · 4 Hours to complete
Core Concepts of Unsupervised Learning, PCA & Dimensionality Reduction
Module 2 · 4 Hours to complete
Data Visualization & Clustering
Module 3 · 4 Hours to complete
Sequence Modeling and Reinforcement Learning
Module 4 · 4 Hours to complete
Fee Structure
Instructor
Research Professor of Financial Machine Learning at NYU Tandon School of Engineering
Igor Halperin is a former Research Professor of Financial Machine Learning at NYU Tandon School of Engineering, specializing in applying advanced methods from reinforcement learning, information theory, neuroscience, and physics to financial problems. His research focuses on areas such as portfolio optimization, dynamic risk management, and the inference of sequential decision-making processes of financial agents. With extensive industrial experience in statistical and financial modeling, Igor has worked in areas like option pricing, credit portfolio risk modeling, and operational risk modeling. He previously held the position of Executive Director of Quantitative Research at JPMorgan and served as a quantitative researcher at Bloomberg LP. Igor has published widely in finance and physics journals and is a frequent speaker at financial conferences. He is also the co-author of Credit Risk Frontiers, published by Bloomberg LP. Holding a Ph.D. in theoretical high energy physics from Tel Aviv University and an M.Sc. in nuclear physics from St. Petersburg State Technical University, Igor advises several fintech and data science start-ups as well as risk management firms.
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3.7 course rating
335 ratings
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