Delves into challenges of learning from probabilistic models, covering computational complexity, data reconstruction, and statistical gaps.
Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.
Explores simplifying belief propagation equations for pairwise models, reducing computational complexity from order n cubed to order n.
Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
Explores computation with tensor networks, covering joint probability distributions, statistical mechanics, and quantum computation applications.
Covers the Subset Sum problem and the efficient LLL algorithm for finding solutions in lattice basis reduction.
Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.
