PPT-CS b553 : A lgorithms for Optimization and Learning

Bayesian Networks agenda Probabilistic inference queries Topdown inference Variable elimination Probability Queries Given some probabilistic model over variables X Find distribution over . Univariate. optimization. x. f. (x). Key Ideas. Critical points. Direct methods. Exhaustive search. Golden section search. Root finding algorithms. Bisection. [More next time]. Local vs. global optimization. Pritam. . Sukumar. & Daphne Tsatsoulis. CS 546: Machine Learning for Natural Language Processing. 1. What is Optimization?. Find the minimum or maximum of an objective function given a set of constraints:. Gradient descent. Key Concepts. Gradient descent. Line search. Convergence rates depend on scaling. Variants: discrete analogues, coordinate descent. Random restarts. Gradient direction . is orthogonal to the level sets (contours) of f,. : Algorithms . for Optimization and Learning. Global optimization. 1. Agenda: . Global Optimization. Local search, optimization. Branch and bound search. Online . search. 3. Global . Optimization. min . Linear programming, quadratic programming, sequential quadratic programming. Key ideas. Linear programming. Simplex method. Mixed-integer linear programming. Quadratic programming. Applications. Radiosurgery. By Namita Dave. Overview. What are compiler optimizations?. Challenges with optimizations. Current Solutions. Machine learning techniques. Structure of Adaptive compilers. Introduction. O. ptimization . Learning. Structure . Learning. Agenda. Learning probability distributions from . example data. To what extent can Bayes net structure be learned?. Constraint methods (inferring conditional independence). un 10/1. . If you’d like to work with 605 students then indicate this on your proposal.. 605 students: the week after 10/1 I will post the proposals on the wiki and you will have time to contact 805 students and join teams.. Univariate. optimization. x. f. (x). Key Ideas. Critical points. Direct methods. Exhaustive search. Golden section search. Root finding algorithms. Bisection. [More next time]. Local vs. global optimization. Gradient descent. Key Concepts. Gradient descent. Line search. Convergence rates depend on scaling. Variants: discrete analogues, coordinate descent. Random restarts. Gradient direction . is orthogonal to the level sets (contours) of f,. Kai Liu. Purdue University. 1. Andrés Tovar. Indiana Univ. - Purdue Univ. Indianapolis. Emily NutWell. Honda R&D Americas. Duane Detwiler. Honda R&D Americas. Systematic Design Optimization Approach . Applications. Lectures 12-13: . Regularization and Optimization. Zhu Han. University of Houston. Thanks . Xusheng. Du and Kevin Tsai For Slide Preparation. 1. Part 1 Regularization Outline. Parameter Norm Penalties. Bayesian . Networks. agenda. B. ayesian networks. Chain rule for . Bayes . nets. Naïve Bayes models. Independence declarations. D-separation. Probabilistic inference queries. Purposes of . bayesian. Learning. Parameter Learning with Hidden Variables & . Expectation . Maximization. Agenda. Learning probability distributions from . data. in the setting of known structure, . missing data. Expectation-maximization (EM) algorithm.