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Quantum machine learning
Enrollment in this course is by invitation only
An advanced course that takes a deep look at algorithms, and how to apply hybrid quantum-classical models to specific machine learning problems.
About this course
Designed for technical professionals, this course explores how quantum computers ingest and learn from data. Learn about different types of algorithms in the field of quantum machine learning (QML), and how to apply hybrid quantum-classical models to industry use cases such as fraud detection and time series forecasting problems.
Prerequisites
To succeed in this course, you should ideally first complete the Quantum technical foundations course or understand these prerequisites.
- Basic linear algebra: Solve systems of equations with matrices, eigenvalues and eigenvectors, linear transformations, and tensor products.
- Trigonometry and complex numbers: Understand the unit circle and how to translate complex numbers to polar coordinates to calculate phases.
- Python: Write and maintain reliable code and familiarity with Numpy or data-science packages.
- Statistics and probabilities: Comprehend probability and other stochastic notions.
- Complexity theory and the limitations of classical computers: Understand scaling laws and categorizing problems such as P versus NP problems.
- It’s helpful to have knowledge of classical methods for support vector machines and neural networks.
Technical requirements
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