Delves into challenges of learning from probabilistic models, covering computational complexity, data reconstruction, and statistical gaps.
Explores the complexity of linear algebra operations and optimization methods, including Gaussian elimination and the simplex method.
Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.
Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
Explores simplifying belief propagation equations for pairwise models, reducing computational complexity from order n cubed to order n.
Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
