Geometry at Work. Adapted by Dr. Sarah from a talk by Dr. Catherine A. Gorini. Computer Learning To Diagnose and Categorize. We will use these tools: • higher-dimensional vector spaces • convex sets • inner products. Geometry in Learning Kristin P. Bennett

ByAn Introduction to Latent Dirichlet Allocation (LDA). David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research 3 (2003): 993-1022. LDA. A generative probabilistic model for collections of discrete data such text corpora.

ByDimensional reduction, PCA. Curse of dimensionality. The higher the dimension, the more data is needed to draw any conclusion Probability density estimation: Continuous: histograms Discrete: k-factorial designs Decision rules: Nearest-neighbor and K-nearest neighbor.

ByComplex Networks: Models. Lecture 2. Slides by Panayiotis Tsaparas. What is a network? . Network: a collection of entities that are interconnected with links . people that are friends computers that are interconnected web pages that point to each other proteins that interact.

ByShape-Representation. and. Shape Similarity. Part 1: Shapes. Dr. Rolf Lakaemper. May I introduce myself…. Rolf Lakaemper PhD (Doctorate Degree) 2000 Hamburg University, Germany Currently Assist. Professor at Department of Computer and Information Sciences,

ByNew Optimality Conditions and Methods for State-Constrained Elliptic Optimal Control Problems Building Bridges between ODE and PDE Optimal Control Michael Frey , Simon Bechmann, Hans Josef Pesch , Armin Rund Chair of Mathematics in Engineering Sciences University of Bayreuth, Germany

ByStatistical Learning. Dong Liu Dept. EEIS, USTC. Chapter 1. Linear Regression. From one to two Regularization Basis functions Bias-variance decomposition Different regularization forms Bayesian approach. A motivating example 1/2. What is the height of Mount Qomolangma ?

ByLagrangian Relaxation and Network Optimization. Cheng-Ta Lee Department of Information Management National Taiwan University September 29, 2005. Outline. Introduction Problem Relaxations and Branch and Bound Lagrangian Relaxation Technique Lagrangian Relaxation and Linear Programming

ByAnalysis of Algorithms CS 477/677. NP-Completeness Instructor: George Bebis Chapter 34. NP-Completeness. So far we’ve seen a lot of good news!

ByThe Theory of NP-Completeness. Review: Finding lower bound by problem transformation. Problem X reduces to problem Y (X Y ) iff X can be solved by using any algorithm which solves Y. If X Y , Y is more difficult.

ByArtificial Intelligence: Representation and Problem Solving Optimization (1): Optimization and Convex Optimization. 15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu.edu Wean Hall 4126. Logistics. Complete the CMU 'course hours worked' survey. Recap.

ByArtificial Intelligence: Representation and Problem Solving Optimization (3): (Mixed) Integer Linear Programming. 15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu.edu Wean Hall 4126. Recap. Convex optimization is a convex function and is a convex set

ByLecture 2(b) Rational Choice and Demand. Why It Would Probably Be Ok to Sleep Through This Part of the Lecture. The previous lecture described almost everything you need to know to understand demand. You know what demand functions are about, what demand elasticity means and why it matters.

BySeparating hyperplane. Optimal separating hyperplane - support vector classifier. Find the hyperplane that creates the biggest margin between the training points for class 1 and -1. margin. Formulation of the optimization problem. Signed distance to decision border.

ByPattern Recognition. Pattern recognition is:. 1. The name of the journal of the Pattern Recognition Society. 2. A research area in which patterns in data are found, recognized, discovered, …whatever. 3. A catchall phrase that includes. classification clustering data mining

BySometimes it Pays to be Greedy: Greedy Algorithms in Economic Epidemiology Fred Roberts, DIMACS. Optimization Problems in Economic Epidemiology. Many problems in Economic Epi can be formulated as optimization problems: Find a solution that maximizes or minimizes some value.

ByOptimal Location of Multiple Bleed Points in Rankine Cycle. P M V Subbarao Professor Mechanical Engineering Department I I T Delhi. Sincere Efforts for Best Returns…. A MATHEMATICAL MODEL. A. Turbine. B. SG. Y j-11, h bj-1. y j, h bj. Y j-2, h bj-2. C. OFWH. OFWH. OFWH. C.

ByUltrasonic Beam-forming with the Genetic Algorithm. Andrew Fiss, Vassar College Nathan Baxter , Ohio Northern University Jerry Magnan , Florida State University. Abstract.

ByCounting position weight matrices in a sequence & an application to discriminative motif finding. Saurabh Sinha Computer Science University of Illinois, Urbana-Champaign. GENE. A C A G TG A. PROTEIN. Transcriptional Regulation. TRANSCRIPTION FACTOR. GENE. A C A G TG A. PROTEIN.

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