Main.LectureNotes History

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January 11, 2016, at 04:29 PM EST by 129.252.33.75 -
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Required Texbook publisher's webpage

reference textbook: Python Scripting for Computational Science

Python-Scripting-Computational-Science-Engineering

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Lec1_intro.pdf  Δ Introduction to Python

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Will be shared via dropbox.com.

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1page-cheatsheet.pdf  Δ 1 page python cheatsheet

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1page-cheatsheet.pdf  Δ 1 page python cheatsheet

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Lec1_intro.ppt  Δ Introduction to Pattern Recognition

HMM2.ppt Δ

DimRed.ppt Δ dimension reduction

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Lec1_intro.pdf  Δ Introduction to Python

June 15, 2013, at 01:08 AM EST by 129.252.28.17 -
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Texbook publisher's webpage

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Required Texbook publisher's webpage

reference textbook: Python Scripting for Computational Science

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DimRed.ppt Δ dimension reduction

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DimRed.ppt Δ dimension reduction

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Texbook publisher's webpage

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Texbook publisher's webpage

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Python-Scripting-Computational-Science-Engineering

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chapter1.ppt

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Lec1_intro.ppt  Δ Introduction to Pattern Recognition

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Week 1 (Chapter 1)

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Chapter 1. Introduction (ppt) Chapter 2. Supervised Learning (ppt) Chapter 3. Bayesian Decision Theory (ppt) Chapter 4. Parametric Methods (ppt) Chapter 5. Multivariate Methods (ppt) Chapter 6. Dimensionality Reduction (ppt) Chapter 7. Clustering (ppt) Chapter 8. Nonparametric Methods (ppt) Chapter 9. Decision Trees (ppt) Chapter 10. Linear Discrimination (ppt) Chapter 11. Multilayer Perceptrons (ppt) Chapter 12. Local Models (ppt) Chapter 13. Hidden Markov Models (ppt) Chapter 14. Assessing and Comparing Classification Algorithms (ppt) Chapter 15. Combining Multiple Learners (ppt) Chapter 16. Reinforcement Learning (ppt)

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January 07, 2013, at 04:52 PM EST by 10.30.13.73 -
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course slides

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Course slides

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chapter2.ppt

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Week 6

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Week7

lec9.ppt Δ Linear discriminant

Week8

lec10.ppt Δ Neural networks

Week9

lec11.ppt Δ GA, GP

Week10

Midterm March 15.

Week11

lec13.ppt Δ SVM

Week12

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ensemble.ppt Δ Ensemble Learning Algorithms

Week15

lec17.ppt Δ Reinforcement Learning

Richard Sutton's Talk on RL

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lec18.ppt Δ Graphical models

January 07, 2013, at 04:50 PM EST by 10.30.13.73 -
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Download Textbook lecture notes

VideoLectures Online video on RL

Please download the above textbook slides. My slides are based on theirs with minor modification

to:

Texbook publisher's webpage

course slides

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April 12, 2012, at 02:27 PM EST by 129.252.11.245 -
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lec16.ppt Δ Ensemble Learning Algorithms

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DimRed.ppt Δ dimension reduction

ensemble.ppt Δ Ensemble Learning Algorithms

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Download Textbook lecture notes

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Download Textbook lecture notes

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lec18.ppt Δ Graphical models

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VideoLectures Online video on RL

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Richard Sutton's Talk on RL

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Week15

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lec17.ppt Δ Reinforcement Learning

Week16

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lec16.ppt Δ Ensemble Learning Algorithms

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lec14.ppt Δ evaluation of classifers

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lec11.ppt Δ GA, GP

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March 02, 2010, at 12:11 AM EST by 75.183.182.225 -
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midterm1

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lec10.ppt Δ Neural networks

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Midterm March 15.

February 25, 2010, at 03:44 AM EST by 75.183.182.225 -
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lec9.ppt Δ Linear discriminant

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chapter2.ppt

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chapter1.ppt

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Textbook lecture notes

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Download Textbook lecture notes

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Week 1

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Chapter 1. Introduction (ppt) Chapter 2. Supervised Learning (ppt) Chapter 3. Bayesian Decision Theory (ppt) Chapter 4. Parametric Methods (ppt) Chapter 5. Multivariate Methods (ppt) Chapter 6. Dimensionality Reduction (ppt) Chapter 7. Clustering (ppt) Chapter 8. Nonparametric Methods (ppt) Chapter 9. Decision Trees (ppt) Chapter 10. Linear Discrimination (ppt) Chapter 11. Multilayer Perceptrons (ppt) Chapter 12. Local Models (ppt) Chapter 13. Hidden Markov Models (ppt) Chapter 14. Assessing and Comparing Classification Algorithms (ppt) Chapter 15. Combining Multiple Learners (ppt) Chapter 16. Reinforcement Learning (ppt)

Week 1 (Chapter 1)

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Week 2 (Chapter 2)

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Midterm1 discussion

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lect15.ppt Δ

Midterm2

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January 14, 2010, at 03:56 PM EST by 129.252.11.239 -
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Please download the above textbook slides. My slides are based on theirs with minor modification

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Please download the above textbook slides. My slides are based on theirs with minor modification

January 14, 2010, at 03:56 PM EST by 129.252.11.239 -
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Please download the above textbook slides. My slides are based on theirs with minor modification

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Please download the above textbook slides. My slides are based on theirs with minor modification

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Week12 11/03, 11/05

No class on 11/03

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Week12

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January 14, 2010, at 02:17 PM EST by 129.252.11.239 -
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Please download the above textbook slides. My slides are based on theirs with minor modification

January 11, 2010, at 10:21 AM EST by 129.252.11.239 -
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lect1.ppt Δ Motivation, steps for algorithm design

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lect2.ppt Δ problem types and data structures

lect3.ppt Δ Theoretical Analysis of Time Efficiency

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lect4.ppt Δ Running time analysis, non-recursive algo

lect5.ppt Δ Running time, recursive algorithm

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lect6.ppt Δ Homogeneous Second-Order Linear Recurrence, brute-force algorithm

lect7.ppt Δ brute force strategy, TSP, assignment, sorting

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lect8.ppt Δ Divide and conquer, mergesort, quicksort

lect9.ppt Δ Divide and conquer

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lect11.ppt Δ Divide and conquer: Closest-Pair Problem, convex-hull

Week8 10/06

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Week9 10/13, 10/15

lect12.ppt Δ Decrease and conquer. a^n

lect13.ppt Δ DFS

Week10 10/20, 10/22

lect13.ppt Δ BFS and Topological sorting

lect14.ppt Δ ch6a

Week11 10/27, 10/29

lect15.ppt Δ ch6b

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December 21, 2009, at 05:16 PM EST by 129.252.11.239 -
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Textbook lecture notes

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lecture notes from textbook

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lecture notes from textbook

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sumMotifScores.txt Δ Motivation, steps for algorithm design

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Week15 11/24

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lect13.ppt Δ BFS and Topological sorting

lect14.ppt Δ ch6a

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lect16.ppt Δ

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lect14.ppt Δ

lect15.ppt Δ

Week11 10/27, 10/29

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Midterm2

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lect9.ppt Δ Divide and conquer

Week 6 09/22, 09/24

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midterm1

lect10.ppt Δ

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lect11.ppt Δ Divide and conquer: Closest-Pair Problem, convex-hull

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lect12.ppt Δ Decrease and conquer. a^n

lect13.ppt Δ DFS

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sumMotifScores.txt Δ Motivation, steps for algorithm design

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sumMotifScores.txt Δ Motivation, steps for algorithm design

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sumMotifScores.pl Δ Motivation, steps for algorithm design

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sumMotifScores.pl Δ Motivation, steps for algorithm design

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sumMotifScores.txt Δ Motivation, steps for algorithm design

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sumMotifScores.pl Δ Motivation, steps for algorithm design

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lect1.ppt Δ Motivation, steps for algorithm design

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lect2.ppt Δ problem types and data structures

lect3.ppt Δ Theoretical Analysis of Time Efficiency

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lect6.ppt Δ Homogeneous Second-Order Linear Recurrence, brute-force algorithm

lect7.ppt Δ brute force strategy, TSP, assignment, sorting

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lect8.ppt Δ Divide and conquer, mergesort, quicksort

lect9.ppt Δ

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Week 3

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Week 3

lect4.ppt Δ Running time analysis, non-recursive algo

lect5.ppt Δ Running time, recursive algorithm

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Lecture1.ppt Δ Introduction to Parallel Computing

Lecture1b.ppt Δ PBS and Linux cluster

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to uncompress: tar zxvf labsession1.tar.gz

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Lecture2.ppt Δ Parallel Architecture

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Lecture3.ppt Δ Parallel algorithm design model

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Lecture4.ppt Δ MPI programming: the basics

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Lecture6.ppt Δ 2D array problem decomposition strategy

MPIsamples.zip Δ Sample MPI source codes in text book

Lecture9.ppt Δ Manager/Worker model for embarassingly parallel problems. Document classification

Lab session 2 lab2.tar.gz Δ Lab session files to uncompress: tar zxvf lab2.tar.gz

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gridcomputing.ppt Δ grid computing

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Lecture11.ppt Δ Matrix multiplication

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Lecture18Hadoop.ppt Δ

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Lecture18.ppt Δ Hybrid MPI and OpenMP programming

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Lecture17.ppt Δ Hadoop and OpenMPI

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Week9

No class

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Lecture7.ppt Δ Performance analysis

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Lecture11.ppt Δ Matrix multiplication

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Lecture10.ppt Δ Monte carlo parallel algorithm

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gridcomputing.ppt Δ grid computing

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Lecture5.ppt Δ 1D array problem decomposition strategy

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Lecture5.ppt Δ 1D array problem decomposition strategy

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Lecture9.ppt Δ Manager/Worker model for embarassingly parallel problems. Document classification Lab session 2

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lab2.tar.gz Δ Lab session files to uncompress: tar zxvf lab2.tar.gz

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MPIsamples.zip Δ Sample MPI source codes in text book

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Week 5

Lecture5.ppt Δ 1D array problem decomposition strategy

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Lecture6.ppt Δ 2D array problem decomposition strategy

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Lecture4.ppt Δ MPI programming: the basics

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Lecture3.ppt Δ Parallel algorithm design model

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Lecture1.ppt Δ Introduction to Parallel Computing

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Lecture2_paul.ppt Δ PBS and Linux cluster

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Lecture1b.ppt Δ PBS and Linux cluster

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Lecture2_paul.ppt Δ PBS and Linux cluster

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Lecture2.ppt Δ Parallel Architecture

Week 3

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Download labsession.tar.gz Δ Lab session program sample code

to uncompress: tar zxvf labsession.tar.gz

to:

Download labsession1.tar.gz Δ Lab session program sample code

to uncompress: tar zxvf labsession1.tar.gz

January 12, 2009, at 06:07 PM EST by 125.34.53.8 -
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 Lecture2.ppt Δ PBS and Linux cluster
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 Lecture2_paul.ppt Δ PBS and Linux cluster
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 Lecture2.ppt Δ PBS and Linux cluster
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Download Lab session Attach:program code Δ

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Download labsession.tar.gz Δ Lab session program sample code to uncompress: tar zxvf labsession.tar.gz

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Lecture1.ppt Δ Data mining overview

Lecture2.ppt Δ K-nearest neighbor/weka

Week2

Lecture3.ppt Δ preprocessing

Week 3

Lecture4.ppt Δ decision tree

Lecture5.ppt Δ Decision Tree & Classifier Evaluation

Week4

Lecture5.ppt Δ Model Evaluation

Lecture6.ppt Δ Bayesian Classifier and ANN

Week5

Lecture7.ppt Δ SVM

Lecture8.ppt Δ Ensemble algorithms

Week6

Lecture9.ppt Δ Clustering/K-means

Lecture10.ppt Δ Hiearchical/Density clustering

Week7

Lecture11.ppt Δ Clustering evaluation/validation Midterm

Reference slides

Slides from Textbook(Tan)

Slides from Textbook (Han)

Statistical Data Mining Tutorials by Andrew Moore at CMU (recommended)

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Midterm

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Week6

Lecture9.ppt Δ Clustering/K-means

Lecture10.ppt Δ Hiearchical/Density clustering

Week7

Lecture11.ppt Δ Clustering evaluation/validation

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Lecture1.ppt Δ Data mining overview

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Lecture4.ppt Δ

Lecture5.ppt Δ Decision Tree & Classifier Evaluation

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Lecture5.ppt Δ Model Evaluation

Lecture6.ppt Δ Bayesian Classifier and ANN

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Happy Thanksgiving

Week14

Lecture24.ppt Δ

Lecture25.ppt Δ

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Lecture9.ppt Δ (Pls download and rename to Lecture9.zip)

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Slides from Textbook (Han)

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Slides from Textbook (Han)

Statistical Data Mining Tutorials by Andrew Moore at CMU (recommended)

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Slides from Textbook(Tan)

Slides from Textbook (Han)

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Reference slides

Slides from Textbook(Tan)

Slides from Textbook (Han)

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Slides from Textbook(Tan)

Slides from Textbook (Han)

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(:attachlist:)

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Week 1

Week 2

Week 3

Week 4

Week 5

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Lecture notes/slides will be uploaded during the course.

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Lecture notes/slides will be uploaded during the course.

(:attachlist:)

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Lecture notes/slides will be uploaded during the course.

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