Quantum Formalism (QF) Academy

Quantum Formalism Academy presents a course on TDA with Applications to Machine Learning. This course bridges advanced mathematical theory with practical applications in modern computing. The course now begins on June 27th, with limited spots available to ensure a small, focused cohort.

Topics Highlight:

• Introduction to TDA:Motivation, basic topology concepts, simplicial complexes.
• Simplicial Homology: Chains, cycles, homology groups, and computational tools.
• Persistent Homology:Stability, filtrations, and persistence modules.
• Computing Persistent Homology: Algorithms, persistence diagrams, and visualization techniques.
• Topological Features in Data Analysis:Mapper algorithm, Reeb graphs, and topological signatures.
• Statistical TDA:Statistical methods, kernel techniques, and hypothesis testing in persistence diagrams.
• TDA in Machine Learning: Integration with ML models, topological neural networks, and graph-based learning.
• TDA in Quantum Computing: topological approaches in quantum computing (e.g., topological quantum error correction, quantum state space analysis).
• Advanced Topics:Multiparameter persistence, persistent cohomology, and category-theoretic approaches.

What This Course Offers:

• Self-paced video lectures.
• Quizzes to test your knowledge.
• In-depth coverage of both theoretical and practical aspects of TDA.
• Interactive Jupyter notebooks with visualizations.
• Live office hour sessions with the instructor for clarifications and tutorials.
• Project-based certification upon completion.

Who Should Take It?

• Researchers and engineers in Machine Learning, and related fields.
• Students and professionals eager to learn advanced mathematics for emerging technologies.

How to sign up (for free)?

You can get free automatic access to the course if sign up to one of the following plans:

Professional Plan OR Researcher Plan

How to sign up with a one off deal?

You can sign up without subscribing to our plans:

Early-bird for just $260