Quantum Machine Learning: Theory and Applications

Related lectures (31)

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Covers inference rules in propositional logic and common logical fallacies.

Covers quantum computing basics, quantum algorithms, error correction, and quantum bit manipulation.

Covers the interpretation of propositional logic and inference rules for implication, conjunction, and double negation.

Explores time-dependent perturbation theory in quantum physics from an interaction point of view.

Explores investigating quantum matter with ultracold gases and the quest for supersolidity.

Explores cognitive load, metacognition, learning environments, and inductive reasoning, with examples of exam questions and the city of Godthåb.

Introduces the principle of mathematical induction through an example.

Delves into the origins of religious beliefs, exploring psychological, developmental, and neuroscientific perspectives on faith.

Covers projectors on invariant subspaces in quantum physics and their applications.

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.