Found 5 relevant results in 2.11s where lecturer="Titus Mangham-Neupert"
This course provides an introduction to simulation methods for quantum systems. Starting from the one-body problem, a special emphasis is on quantum many-body problems, where we cover both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, matrix product states, and quantum Monte Carlo methods).
The course is addressed to students in experimental and theoretical condensed matter physics and provides a theoretical introduction to a variety of important concepts used in this field.
The course covers the scientific method with an emphasis on critical thinking, linking it to the AI-driven paradigm shift. It introduces key AI terms and explores how AI can aid scientific discovery and speed workflows in quantitative fields, examining practical use cases, biases, and potential pitfalls.
This course is an introduction to the basic concepts of machine learning, including supervised and unsupervised learning with neural networks, reinforcement learning, and methods to make the learned results interpretable. The material is presented with scientific research applications in mind, where data has often very peculiar structure and quantitative accuracy is paramount.
This course provides the student with a solid understanding of quantum phases with non-trivial topological properties. At the end of the course the student will be acquainted with the theoretical description of the integer and fractional quantum Hall phases, symmetry protected topological states like the topological insulators and quantum spin systems.