ISyE 7406
Data Mining & Statistical Learning
Spring 2016
Yajun Mei ([email protected])
Team Project
For the project, you are encouraged to work in a team of 2 5 students, but it will be ne if you p
ISyE 7406: Data Mining & Statistical Learning
HW#3
(due @9:25am on Wednesday, Feb 24, 2016 at T-square for on-campus students,
and one week delay for DL students)
Problem 1 (50 points). In this proble
Learning to Set Up Numerical
Optimizations of Engineering Design
Chaitanya Mudunuri
Kartick Narasimhadevara
Agenda
Concept.
Design of Racing Yacht.
Design of Supersonic Transport Aircraft.
Conclusion.
To appear in the proceedings of IJCAI-95
Multiresolution Instance-Based Learning
Kan Deng and Andrew W. Moore
The Robotics Institute, Smith Hall 221
Carnegie Mellon University,
phone: (412) 268-7599
k
Training fMRI Classiers to Detect Cognitive
States across Multiple Human Subjects
Xuerui Wang, Rebecca Hutchinson, and Tom M. Mitchell
Center for Automated Learning and Discovery
Carnegie Mellon Unive
Project 2:
Literature Review of Market Basket
Study of Financial Product
Shan Huang, Zhao Liu, Min Zhang
Abstract
In the financial industry, there are millions of financial products. But more than
80%
Research
Entrepreneurships
Professor J.-C. Lu
Industrial and Systems Engineering
Georgia Institute of Technology
Table of Content
Motivation
Thesis Research
Semiconductor and Electronics
Manufacturing
RESEARCH PROFILING IMPROVING THE LITERATURE REVIEW:
ILLUSTRATED FOR THE CASE OF DATA MINING OF LARGE DATASETS
Alan L. Porter, Alisa Kongthon, J.C. Lu
Text mining can augment review of prior research t
Detecting Signicant Multidimensional Spatial
Clusters
Daniel B. Neill, Andrew W. Moore, Francisco Pereira, and Tom Mitchell
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
n
About the National Science and Technology Council
The National Science and Technology Council (NSTC) was established by Executive Order on November 23, 1993. The
Cabinet-level council is the principal
Chapter4 LinearMethodsfor
Classification
ISyE7406classpresentation
Prabuddha Bansal
School of Chemical and Biomolecular Engineering
Georgia Institute of Technology
27th January, 2010
1
Introduction
Li
RESEARCH PROFILING IMPROVING THE LITERATURE REVIEW:
ILLUSTRATED FOR THE CASE OF DATA MINING OF LARGE DATASETS
Alan L. Porter, Alisa Kongthon, J.C. Lu
Text mining can augment review of prior research t
Dear INFORMS - Atlanta Community,
I specialize in Public Safety/Sector Recruitment and am networking to find a particular
person for a Retained search for a Lead Operations Analyst.
I am seeking someo
Laplace Approximation
Thursday, September 11, 2008 Rice University STAT 631 / ELEC 639: Graphical Models Instructor: Dr. Volkan Cevher Scribe: Ryan Guerra Reviewers: Beth Bower and Terrance Savitsky
I
Data Mining for Design
and Manufacturing
Methods and Applications
Edited by
Dan Braha
Ben-Gurion University
KLUWER ACADEMIC PUBLISHERS
DORDRECHT/ BOSTON / LONDON TABLE OF CONTENTS
A C.I.P. Catalogue r
System Modeling Example A fast-food Preparation Process
Goal: Design A Data Collection Scheme to
(1) validate product quality, or
(2) find the best process recipe.
Background: There exist
K number of
6.975 Week 11 Summary:
Expectation Propagation
Erik Sudderth
November 20, 2002
1
Introduction
Expectation Propagation (EP) [2, 3] addresses the problem of constructing tractable approximations to comp
Optimal Engineering System Design Guided by Data-mining Methods
Pansoo Kim and Yu Ding*
Department of Industrial Engineering
Texas A&M University, 3131 TAMU
College Station, TX 77843-3131
Abstract: An
Distributed Sensing for Quality and Productivity Improvements*
Yu Ding
Department of Industrial Engineering
Texas A&M University
College Station, TX 77843-3131
Elsayed A. Elsayed
Department of Industr
September 05, 2002
BIOMEDICAL: Risks Weighed as Products Enter Market
Nanomaterials are bringing biomedical innovations in diagnostics, drugs and prostheses.
Dangers lurk, however, as nanoparticles ca
Learning Common Features from fMRI Data of Multiple Subjects
John Ramish
Advised by Prof. Tom Mitchell
8/10/04
Abstract
Functional Magnetic Resonance Imaging (fMRI), a brain imaging technique, has
all
Importance Sampling Theory
P ( E = e) = P ( Z = z , E = e)
zZ
Given a distribution called the proposal distribution
Q (such that P(Z=z,e)>0=> Q(Z=z)>0)
P ( Z = z , E = e)
Q( Z = z )
P ( E = e) =
Q(
Journal of Machine Learning Research 5 (2004) 239253
Submitted 9/02; Revised 7/03; Published 3/04
Weather Data Mining Using
Independent Component Analysis
Jayanta Basak
BJAYANTA @ IN . IBM . COM
IBM I
Research Proling - Improving the Literature
Review: Illustrated for the Case of Data Mining
of Large Datasets
Brian Gilbert
ISYE 7406
January 20, 2010
Outline
Introduction
Research Proling
An Illustra
FEBRUARY 2003
In labs around the world, researchers are busy
creating technologies that will change the way
we conduct business and live our lives. These
are not the latest crop of gadgets and gizmos:
ISyE 7406: Data Mining & Stat Learning
Week #14
Review of supervised + unsupervised learning
Sequential or streaming data analysis
Upcoming Deadlines for On-campus students
1. Project presentation f
Solution of ISyE 7406 Homework 3
February 28, 2017
Problem 1
Grading Policy
1. Technical Part (20 points). Most students knew how to implement our methods in R
and were able to obtain R results. Howev
Notes on Support Vector Machine
Yajun Mei ([email protected])
This note is from the book Principles and Theory for Data Mining and Machine Learning by Clarke,
Fokoue and Zhang (2009).
The core idea
ISyE 7406A: Data Mining & Stat Learning
Comments on Midterm
Final Course project: Peer evaluations
Presentation evaluation (other teams)
Teammate Evaluation (within team)
Midterms
Most students d
ISyE 7406A: Data Mining & Stat Learning
Cluster Analysis (Ch 14.3)
K-means Clustering (Ch 14.3.6)
EM Algorithm (Ch 8.5)
Hierarchical Clustering (Ch 14.3.12)
MM Algorithm
Random graph models for n