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August 19, 2017, at 11:14 AM EST by 76.213.117.150 -
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Barbara 803 777-7849 (phone)

to:

803 777-7849 (phone)

August 19, 2017, at 11:14 AM EST by 76.213.117.150 -
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CSCE 206 Scientific Application Programming Spring 2017

to:

CSCE 206 Scientific Application Programming Fall 2017

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Classroom: 300 Main St. B201

to:

Classroom: Horizon Parking Garage 210

January 22, 2017, at 11:52 PM EST by 65.87.143.36 -
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Office Hours: MW 2:10AM-3:00AM or by Appointment.

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Office Hours: TTH 3:0AM-4:00AM or by Appointment.

January 22, 2017, at 10:07 PM EST by 65.87.143.36 -
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A Primer on Scientific Programming with Python by Hans Petter Langtangen (2012)
ISBN-10: 3642302920 Springer;

to:

A Primer on Scientific Programming with Python by Hans Petter Langtangen (2014)
ISBN-10: 3642549586 Springer;

January 11, 2017, at 04:45 PM EST by 129.252.33.94 -
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Meeting Time: TTH 1:15 am - 2:30 am
Lab time (Starting from 2nd week)
Classroom: SWGN 2A14

to:

Meeting Time: TTH 10:05 am - 11:25 am
Classroom: 300 Main St. B201

January 11, 2017, at 04:37 PM EST by 129.252.33.94 -
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CSCE 206 Scientific Application Programming Spring 2016

to:

CSCE 206 Scientific Application Programming Spring 2017

January 10, 2016, at 09:33 PM EST by 24.40.214.249 -
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CSCE 206 Scientific Application Programming Fall 2015

to:

CSCE 206 Scientific Application Programming Spring 2016

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Meeting Time: MWF 9:40 am - 10:30 am
Lab time Friday 9:40-10:30am 1D29 (Starting from 2nd week)\\

to:

Meeting Time: TTH 1:15 am - 2:30 am
Lab time (Starting from 2nd week)\\

November 03, 2015, at 09:58 AM EST by 129.252.33.98 -
Changed line 61 from:

If you have problem to enroll, pls contact/call CSE secretary

to:

If you have need overwrite request to enroll, check it here

August 19, 2015, at 08:44 AM EST by 129.252.33.9 -
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Lab time Friday 9:40-10:30am 1D29 (Starting from 2nd week)

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Lab time Friday 9:40-10:30am 1D29 (Starting from 2nd week)\\

August 19, 2015, at 08:43 AM EST by 129.252.33.9 -
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Lab time Friday 9:40-10:30am 1D29 (Starting from 2nd week)

August 19, 2015, at 08:16 AM EST by 129.252.33.9 -
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Office Hours: TTH 2:10AM-3:00AM or by Appointment.

to:

Office Hours: MW 2:10AM-3:00AM or by Appointment.

August 19, 2015, at 07:55 AM EST by 129.252.33.9 -
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CSCE 206 Scientific Application Programming Spring 2015

to:

CSCE 206 Scientific Application Programming Fall 2015

Changed lines 14-16 from:

Meeting Time: TTH 8:30 am - 9:45 am
Classroom: 300 Main St B110

to:

Meeting Time: MWF 9:40 am - 10:30 am
Classroom: SWGN 2A14

Changed lines 26-30 from:

Office Hours: TTH 2:00AM-3:00AM or by Appointment.

to:

Office Hours: TTH 2:10AM-3:00AM or by Appointment.

January 08, 2015, at 03:37 PM EST by 10.30.124.172 -
Changed lines 26-30 from:

Office Hours: TTH 10:00AM-11:30AM or by Appointment.

to:

Office Hours: TTH 2:00AM-3:00AM or by Appointment.

January 08, 2015, at 03:36 PM EST by 10.30.124.172 -
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CSCE 206 Scientific Application Programming Fall 2014

to:

CSCE 206 Scientific Application Programming Spring 2015

September 25, 2014, at 01:25 PM EST by 10.31.14.210 -
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Office: 3A66 Swearinger Engineering Center\\

to:

Office: 3A47 Swearinger Engineering Center\\

August 18, 2014, at 09:36 PM EST by 24.40.214.249 -
Changed lines 1-2 from:

CSCE 206 Scientific Application Programming Fall 2013

to:

CSCE 206 Scientific Application Programming Fall 2014

Changed lines 14-16 from:

Meeting Time: TTH 10:05 am - 11:20 am
Classroom: 2A21 Swearinger Engineering Center

to:

Meeting Time: TTH 8:30 am - 9:45 am
Classroom: 300 Main St B110

Changed lines 26-30 from:

Office Hours: TTH 2:00PM-3:00PM or by Appointment.

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Office Hours: TTH 10:00AM-11:30AM or by Appointment.

August 21, 2013, at 01:40 PM EST by 10.31.14.210 -
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Classroom: 2A22 Swearinger Engineering Center

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Classroom: 2A21 Swearinger Engineering Center

August 16, 2013, at 03:57 PM EST by 10.31.14.210 -
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Meeting Time: TTH 11:00AM-12:15PM\\

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Meeting Time: TTH 10:05 am - 11:20 am \\

June 17, 2013, at 10:44 AM EST by 10.31.14.210 -
Changed lines 39-48 from:
  1. Input data
    #Arrays
    #Files, strings, dictionaries
    #Graphs, ploting, visualization
    #Classes
    #Random numbers
    #Object-oriented programming
    #Sequences and difference equation
    #Discrte calculus
    #Solving Differential equations\\
to:
  1. Input data
  2. Arrays
  3. Files, strings, dictionaries
  4. Graphs, plotting, visualization
  5. Classes
  6. Random numbers
  7. Object-oriented programming
  8. Sequences and difference equation
  9. Discrete calculus
  10. Solving Differential equations
June 17, 2013, at 10:44 AM EST by 10.31.14.210 -
Changed lines 37-38 from:
  1. Loops and lists
    #Functions and branching\\
to:
  1. Loops and lists
  2. Functions and branching
June 17, 2013, at 10:43 AM EST by 10.31.14.210 -
Changed lines 38-53 from:

Functions and branching
Input data
Arrays
Files, strings, dictionaries
Graphs, ploting, visualization
Classes
Random numbers
Object-oriented programming
Sequences and difference equation
Discrte calculus
Sovling Differential equations
parallel computing with python

to:
  1. Functions and branching
    #Input data
    #Arrays
    #Files, strings, dictionaries
    #Graphs, ploting, visualization
    #Classes
    #Random numbers
    #Object-oriented programming
    #Sequences and difference equation
    #Discrte calculus
    #Solving Differential equations
    #parallel computing with python
June 17, 2013, at 10:43 AM EST by 10.31.14.210 -
Changed lines 37-51 from:

1 Introduction
2 Bayesian Decision Theory
3 Maximum Likelihood and Bayesian Parameter estimation
4 nonParametric Methods
5 Linear Discrimination 85
6 Multilayer neural network 105
7 Stochastic methods: search boltzman machines, Genetic algorithms
8 Nonmetric Methods
9 unsupervised learning and clustering
10 Dimensionality Reduction
13 Hidden Markov Models

to:
  1. Loops and lists
    Functions and branching
    Input data
    Arrays
    Files, strings, dictionaries
    Graphs, ploting, visualization
    Classes
    Random numbers
    Object-oriented programming
    Sequences and difference equation
    Discrte calculus
    Sovling Differential equations
    parallel computing with python
June 15, 2013, at 01:43 AM EST by 129.252.28.17 -
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http://matplotlib.org/_images/wire3d_animation_demo.png

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June 15, 2013, at 01:43 AM EST by 129.252.28.17 -
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to:

http://matplotlib.org/_images/wire3d_animation_demo.png

June 15, 2013, at 01:42 AM EST by 129.252.28.17 -
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http://matplotlib.org/_images/findobj_demo1.png

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June 15, 2013, at 01:42 AM EST by 129.252.28.17 -
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to:

http://matplotlib.org/_images/findobj_demo1.png

June 15, 2013, at 01:41 AM EST by 129.252.28.17 -
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http://matplotlib.org/_images/tricontour_demo_001.png

June 15, 2013, at 01:40 AM EST by 129.252.28.17 -
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to:

June 15, 2013, at 12:52 AM EST by 129.252.28.17 -
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This course will cover the techniques and topics that are widely used in real-world pattern recognition. It will prepare you with skills for working in companies such as Google, Microsoft, Amazon, IBM, and many other business intelligence enterprises. Pattern recognition ( along with Machine learning or data mining) is at the center of information revolution.

to:

This course will cover the techniques and topics that are widely used in real-world scientific application programming. It will prepare you with skills for working in companies such as Google, Microsoft, Amazon, IBM, and many other business intelligence enterprises.

June 15, 2013, at 12:52 AM EST by 129.252.28.17 -
Changed lines 18-20 from:

Pattern Classification (2nd Edition) by Duda and Hart (2001)
ISBN-10: 2nd edition 0471056693 Wiley-Interscience;

to:

A Primer on Scientific Programming with Python by Hans Petter Langtangen (2012)
ISBN-10: 3642302920 Springer;

June 15, 2013, at 12:51 AM EST by 129.252.28.17 -
Changed lines 3-4 from:

Why pattern recognition? Pattern recognition is the key technique for understanding the data and for making intelligent decisions. A major focus of pattern recognition research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.

to:

We are going to learn scientific application programming using Python. Why python? As an object oriented high-level language with a large number of libraries, python allows us to quickly develop high-quality and powerful scientific applications. It is also a language that prevail in web programming and many other related areas.

June 15, 2013, at 12:48 AM EST by 129.252.28.17 -
Changed lines 1-2 from:

CSCE 768 Pattern Recognition Spring 2013

to:

CSCE 206 Scientific Application Programming Fall 2013

January 07, 2013, at 05:44 PM EST by 10.30.13.73 -
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http://mycounter.tinycounter.com/index.php?user=gespim

to:

http://www.tinycounter.com

January 07, 2013, at 05:35 PM EST by 10.30.13.73 -
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search engines, Amazon recommendation systems, spam filter, network intrusion detection systems, bioinformatics, computational biology, machine translation, robotics, medical diagnosis, speech and image recognition, biometrics, finance.

to:

search engines, financial investment and trading, Amazon recommendation systems, spam filter, network intrusion detection systems, bioinformatics, computational biology, machine translation, robotics, medical diagnosis, speech and image recognition, biometrics, finance.

January 07, 2013, at 05:35 PM EST by 10.30.13.73 -
Changed lines 3-4 from:

Why pattern recognition? Pattern recognition is the key technique for understanding the data and for converting data into knowledge and intelligence. A major focus of pattern recognition research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.

to:

Why pattern recognition? Pattern recognition is the key technique for understanding the data and for making intelligent decisions. A major focus of pattern recognition research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.

January 07, 2013, at 05:29 PM EST by 10.30.13.73 -
Changed lines 31-32 from:

This course will cover the techniques and topics that are widely used in real-world businesses. It will prepare you with skills for working in companies such as Google, Microsoft, Amazon, IBM, and many other business intelligence enterprises. Machine learning (along with data mining) is at the center of information revolution.

to:

This course will cover the techniques and topics that are widely used in real-world pattern recognition. It will prepare you with skills for working in companies such as Google, Microsoft, Amazon, IBM, and many other business intelligence enterprises. Pattern recognition ( along with Machine learning or data mining) is at the center of information revolution.

Changed lines 37-55 from:

2 Supervised Learning 17
3 Bayesian Decision Theory 39
4 Parametric Methods 61
5 Multivariate Methods 85
6 Dimensionality Reduction 105
7 Clustering 133
8 Nonparametric Methods 153
9 Decision Trees 173
10 Linear Discrimination 197
11 Multilayer Perceptrons 229
12 Local Models 275
13 Hidden Markov Models 305
14 Assessing and Comparing Classification Algorithms 327
15 Combining Multiple Learners 351
16 Reinforcement Learning 373

to:

2 Bayesian Decision Theory
3 Maximum Likelihood and Bayesian Parameter estimation
4 nonParametric Methods
5 Linear Discrimination 85
6 Multilayer neural network 105
7 Stochastic methods: search boltzman machines, Genetic algorithms
8 Nonmetric Methods
9 unsupervised learning and clustering
10 Dimensionality Reduction
13 Hidden Markov Models

January 07, 2013, at 05:25 PM EST by 10.30.13.73 -
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Meeting Time: TTH TTH 11:00AM-12:15PM\\

to:

Meeting Time: TTH 11:00AM-12:15PM\\

Changed lines 25-29 from:

Office Hours: MW 1:00PM-2:30PM or by Appointment.

to:

Office Hours: TTH 2:00PM-3:00PM or by Appointment.

January 07, 2013, at 05:24 PM EST by 10.30.13.73 -
Changed lines 13-15 from:

Meeting Time: TTH 12:30PM- 1:45PM
Classroom: 2A18 Swearinger Engineering Center

to:

Meeting Time: TTH TTH 11:00AM-12:15PM
Classroom: 2A22 Swearinger Engineering Center

January 07, 2013, at 05:23 PM EST by 10.30.13.73 -
Changed lines 1-5 from:

CSCE 883 Machine Learning Spring 2012

Why machine learning? Machine learning is the key technique for understanding the data and for converting data into knowledge and intelligence. According to Wikipedia Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to change behavior based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Hence, machine learning is closely related to fields such as statistics, probability theory, data mining, pattern recognition, artificial intelligence, adaptive control, and theoretical computer science.

to:

CSCE 768 Pattern Recognition Spring 2013

Why pattern recognition? Pattern recognition is the key technique for understanding the data and for converting data into knowledge and intelligence. A major focus of pattern recognition research is to automatically learn to recognize complex patterns and make intelligent decisions based on data.

Changed lines 18-20 from:

Introduction to Machine Learning by Ethem Alpaydin
ISBN-10: 2nd edition (suggested). 026201243X (Feb 10, 2010) The MIT Press

to:

Pattern Classification (2nd Edition) by Duda and Hart (2001)
ISBN-10: 2nd edition 0471056693 Wiley-Interscience;

January 08, 2012, at 06:38 PM EST by 129.252.11.94 -
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Classroom: 2A24 Swearinger Engineering Center

to:

Classroom: 2A18 Swearinger Engineering Center

January 08, 2012, at 06:38 PM EST by 129.252.11.94 -
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CSCE 883 Machine Learning Spring 2010

to:

CSCE 883 Machine Learning Spring 2012

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Meeting Time: MWF 11:15AM-12:05PM\\

to:

Meeting Time: TTH 12:30PM- 1:45PM\\

Changed line 64 from:

Jewel T. Rogers 803 777-7849 (phone)

to:

Barbara 803 777-7849 (phone)

January 24, 2010, at 07:50 PM EST by 75.183.182.225 -
Added line 36:
January 24, 2010, at 07:49 PM EST by 75.183.182.225 -
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Office Hours: TTH 3:15PM-5:00PM or by Appointment.

to:

Office Hours: MW 1:00PM-2:30PM or by Appointment.

January 21, 2010, at 12:00 PM EST by 129.252.11.239 -
Deleted line 19:

ISBN-10: (required)Older version(2004): ISBN 0262012111. The MIT Press\\

January 20, 2010, at 05:37 PM EST by 129.252.11.94 -
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Classroom: 2A21 Swearinger Engineering Center

to:

Classroom: 2A24 Swearinger Engineering Center

December 21, 2009, at 05:10 PM EST by 129.252.11.239 -
Changed lines 6-9 from:
to:

Application areas:
search engines, Amazon recommendation systems, spam filter, network intrusion detection systems, bioinformatics, computational biology, machine translation, robotics, medical diagnosis, speech and image recognition, biometrics, finance.

December 21, 2009, at 05:06 PM EST by 129.252.11.239 -
Changed line 16 from:

Introduction to Machine Learning by Ethem Alpaydin

to:

Introduction to Machine Learning by Ethem Alpaydin\\

December 21, 2009, at 05:06 PM EST by 129.252.11.239 -
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ISBN-10: (required)Older version(2004): ISBN 0262012111 suggested. 9780262012430 The MIT Press (Feb 10, 2010)

to:

ISBN-10: (required)Older version(2004): ISBN 0262012111. The MIT Press
ISBN-10: 2nd edition (suggested). 026201243X (Feb 10, 2010) The MIT Press

December 09, 2009, at 05:29 PM EST by 129.252.11.239 -
Changed lines 17-19 from:

ISBN-10: 9780262012430 The MIT Press (Feb 1, 2010) (required) Older version(2004): ISBN 0262012111

to:

ISBN-10: (required)Older version(2004): ISBN 0262012111 suggested. 9780262012430 The MIT Press (Feb 10, 2010)

December 09, 2009, at 01:35 PM EST by 129.252.11.239 -
Changed lines 18-19 from:
to:

Older version(2004): ISBN 0262012111

December 09, 2009, at 01:34 PM EST by 129.252.11.239 -
December 09, 2009, at 01:32 PM EST by 129.252.11.239 -
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ISBN-10: 9780262012430 The MIT Press (October 1, 2004) (required)

to:

ISBN-10: 9780262012430 The MIT Press (Feb 1, 2010) (required)

December 09, 2009, at 01:31 PM EST by 129.252.11.239 -
Changed lines 17-19 from:

ISBN-10: 0262012111 The MIT Press (October 1, 2004) (required)

to:

ISBN-10: 9780262012430 The MIT Press (October 1, 2004) (required)

December 07, 2009, at 05:27 PM EST by 129.252.11.239 -
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Supercomputers at USC College of Engineering and Computing(from HPC)

  • Nick Linux clusters with 291 CPUs
  • Optimus Linux clusters with 256 CPUs
  • Zia Share memory computer with 128 CPUs and 256 Shared memory
  • Nataku, Jaws2 (Chemistry Department) for fuel cell simulation and etc.
to:
December 07, 2009, at 05:26 PM EST by 129.252.11.239 -
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Blue-gene SuperComputer with 212992 CPUs

http://graphics8.nytimes.com/images/blogs/bits/posts/supercomputer.533.jpg

to:
December 07, 2009, at 05:23 PM EST by 129.252.11.239 -
Changed lines 34-54 from:

1 Introduction 2 Supervised Learning 17 3 Bayesian Decision Theory 39 4 Parametric Methods 61 5 Multivariate Methods 85 6 Dimensionality Reduction 105 7 Clustering 133 8 Nonparametric Methods 153 9 Decision Trees 173 10 Linear Discrimination 197 11 Multilayer Perceptrons 229 12 Local Models 275 13 Hidden Markov Models 305 14 Assessing and Comparing Classification Algorithms 327 15 Combining Multiple Learners 351 16 Reinforcement Learning 373

to:

1 Introduction
2 Supervised Learning 17
3 Bayesian Decision Theory 39
4 Parametric Methods 61
5 Multivariate Methods 85
6 Dimensionality Reduction 105
7 Clustering 133
8 Nonparametric Methods 153
9 Decision Trees 173
10 Linear Discrimination 197
11 Multilayer Perceptrons 229
12 Local Models 275
13 Hidden Markov Models 305
14 Assessing and Comparing Classification Algorithms 327
15 Combining Multiple Learners 351
16 Reinforcement Learning 373

December 07, 2009, at 05:21 PM EST by 129.252.11.239 -
Changed lines 3-4 from:
to:

Why machine learning? Machine learning is the key technique for understanding the data and for converting data into knowledge and intelligence. According to Wikipedia Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to change behavior based on data, such as from sensor data or databases. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Hence, machine learning is closely related to fields such as statistics, probability theory, data mining, pattern recognition, artificial intelligence, adaptive control, and theoretical computer science.

December 07, 2009, at 05:19 PM EST by 129.252.11.239 -
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TTH 2:00PM-3:15PM\\

to:

Meeting Time: MWF 11:15AM-12:05PM\\

December 07, 2009, at 05:15 PM EST by 129.252.11.239 -
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  1. Introduction
  2. Fundamentals of the Analysis of Algorithm Efficiency
  3. Brute Force
  4. Divide-and-Conquer, Decrease and Conquer, Transform and Conquer
  5. Space and Time Tradeoffs
  6. Dynamic Programming
  7. Greedy Technique
  8. Limitation of Algorithm Power and Coping with the Limitations of Algorithm Power
to:

1 Introduction 2 Supervised Learning 17 3 Bayesian Decision Theory 39 4 Parametric Methods 61 5 Multivariate Methods 85 6 Dimensionality Reduction 105 7 Clustering 133 8 Nonparametric Methods 153 9 Decision Trees 173 10 Linear Discrimination 197 11 Multilayer Perceptrons 229 12 Local Models 275 13 Hidden Markov Models 305 14 Assessing and Comparing Classification Algorithms 327 15 Combining Multiple Learners 351 16 Reinforcement Learning 373

December 07, 2009, at 05:14 PM EST by 129.252.11.239 -
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CSCE 350 Data Structure and Algorithms 2009

to:

CSCE 883 Machine Learning Spring 2010

Changed lines 14-16 from:

Anany Levitin. Introduction to the Design and Analysis of Algorithms. Addison-Wesley, 2nd edition. (required)

to:

Introduction to Machine Learning by Ethem Alpaydin ISBN-10: 0262012111 The MIT Press (October 1, 2004) (required)

Changed lines 28-29 from:

This course will cover the techniques and topics that are widely used in real-world programming. This course is about how to training yourself into becoming a professional programming guru.

to:

This course will cover the techniques and topics that are widely used in real-world businesses. It will prepare you with skills for working in companies such as Google, Microsoft, Amazon, IBM, and many other business intelligence enterprises. Machine learning (along with data mining) is at the center of information revolution.

August 18, 2009, at 09:21 AM EST by 129.252.11.94 -
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Machine Learning and Evolution Group (MLEG)\\

to:

Machine Learning and Evolution Laboratory (MLEG)\\

August 18, 2009, at 09:20 AM EST by 129.252.11.94 -
August 18, 2009, at 09:20 AM EST by 129.252.11.94 -
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This course will cover the techniques and topics that are widely used in real-world parallel computing. This is about learning how to run your programs on hundreds of computers or thousands of CPUs to solve real-world large problems. This is about how to handle large-scale data processing as those used in Google. Students in science and engineering will all benefit from this course as scientific computing has become one of the main ways for discovery and invention.

to:

This course will cover the techniques and topics that are widely used in real-world programming. This course is about how to training yourself into becoming a professional programming guru.

Changed lines 30-37 from:

Many interesting applications, Hands-on projects, exposure to latest techniques. Topics include but not limited to:

  • Linux cluster computing
  • PBS systems
  • Parallel programming using MPI, OpenMP, pthreads
to:

Many interesting topics will be covered:

  1. Introduction
  2. Fundamentals of the Analysis of Algorithm Efficiency
  3. Brute Force
  4. Divide-and-Conquer, Decrease and Conquer, Transform and Conquer
  5. Space and Time Tradeoffs
  6. Dynamic Programming
  7. Greedy Technique
  8. Limitation of Algorithm Power and Coping with the Limitations of Algorithm Power
August 18, 2009, at 09:17 AM EST by 129.252.11.94 -
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Hardcover: 544 pages
Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (June 5, 2003)
Language: English
ISBN-10: 0072822562
ISBN-13: 978-0072822564

to:
August 18, 2009, at 09:16 AM EST by 129.252.11.94 -
Changed lines 14-16 from:

Parallel Programming in C with MPI and OpenMP (Hardcover) by Michael Quinn 2003

to:

Anany Levitin. Introduction to the Design and Analysis of Algorithms. Addison-Wesley, 2nd edition. (required)

Changed lines 31-33 from:

'''Textbook:Anany Levitin. Introduction to the Design and Analysis of Algorithms. Addison-Wesley, 2nd edition. (required)

to:
August 18, 2009, at 09:15 AM EST by 129.252.11.94 -
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CSCE 569 Parallel Computing, Spring 2009

Course-flier.pdf

to:

CSCE 350 Data Structure and Algorithms 2009

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MWF 11:15AM-12:05PM\\

to:

TTH 2:00PM-3:15PM\\

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dd

to:
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Office Hours: MW 1:00PM-2:00PM or by Appointment.

to:

Office Hours: TTH 3:15PM-5:00PM or by Appointment.

'''Textbook:Anany Levitin. Introduction to the Design and Analysis of Algorithms. Addison-Wesley, 2nd edition. (required)

August 18, 2009, at 09:08 AM EST by 129.252.11.94 -
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dd

January 26, 2009, at 10:04 AM EST by 127.0.0.2 -
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Office Hours: MW 1:00PM-2:30PM or by Appointment.

to:

Office Hours: MW 1:00PM-2:00PM or by Appointment.

January 26, 2009, at 10:02 AM EST by 127.0.0.2 -
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Office Hours: MW 1:30PM-3:30PM or by Appointment.

to:

Office Hours: MW 1:00PM-2:30PM or by Appointment.

November 20, 2008, at 03:53 PM EST by 129.252.11.58 -
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Hardcover: 544 pages Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (June 5, 2003) Language: English ISBN-10: 0072822562

to:

Hardcover: 544 pages
Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (June 5, 2003)
Language: English
ISBN-10: 0072822562\\

November 20, 2008, at 03:52 PM EST by 129.252.11.58 -
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to:

Parallel Programming in C with MPI and OpenMP (Hardcover) by Michael Quinn 2003 Hardcover: 544 pages Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (June 5, 2003) Language: English ISBN-10: 0072822562 ISBN-13: 978-0072822564

November 10, 2008, at 11:28 PM EST by 98.25.200.85 -
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Supercomputers at USC College of Engineering (from HPC)

to:

Supercomputers at USC College of Engineering and Computing(from HPC)

October 15, 2008, at 12:49 PM EST by 129.252.11.192 -
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img http://mycounter.tinycounter.com/index.php?user=gespim

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http://mycounter.tinycounter.com/index.php?user=gespim

October 15, 2008, at 12:48 PM EST by 129.252.11.192 -
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<a href="http://www.tinycounter.com" target="_blank" title="free counter"><img border="0" alt="free counter" src="http://mycounter.tinycounter.com/index.php?user=gespim"></a>

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img http://mycounter.tinycounter.com/index.php?user=gespim

October 15, 2008, at 12:47 PM EST by 129.252.11.192 -
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301 Main Street, Columbia, SC, 29201

to:

301 Main Street, Columbia, SC, 29201

<a href="http://www.tinycounter.com" target="_blank" title="free counter"><img border="0" alt="free counter" src="http://mycounter.tinycounter.com/index.php?user=gespim"></a>

October 14, 2008, at 01:28 PM EST by 129.252.11.192 -
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October 14, 2008, at 11:30 AM EST by 127.0.0.2 -
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Supercomputers at USC College of Engineering (from [[http://www.engr.sc.edu/hpcg/symacc.html|HPC

to:

Supercomputers at USC College of Engineering (from HPC)

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Supercomputers at USC College of Engineering (from HPC homagpage)

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Supercomputers at USC College of Engineering (from [[http://www.engr.sc.edu/hpcg/symacc.html|HPC

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Supercomputers at USC College of Engineering (from HPC homagpage)

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Supercomputers at USC College of Engineering (from HPC homagpage)

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Supercomputers at USC College of Engineering

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Supercomputers at USC College of Engineering (from HPC homagpage)

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Supercomputers at USC College of Engineering

  • Nick Linux clusters with 291 CPUs
  • Optimus Linux clusters with 256 CPUs
  • Zia Share memory computer with 128 CPUs and 256 Shared memory
  • Nataku, Jaws2 (Chemistry Department) for fuel cell simulation and etc.
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'''Textbooks

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Textbooks

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'''Textbooks

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Many interesting applications, Hands-on projects, exposure to latest techniques. Topics include:

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Many interesting applications, Hands-on projects, exposure to latest techniques. Topics include but not limited to:

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Many interesting applications, Hands-on projects, exposure to latest techniques. Topics include:\\

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Many interesting applications, Hands-on projects, exposure to latest techniques. Topics include:

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Many interesting applications, Hands-on projects, exposure to techniques from

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Many interesting applications, Hands-on projects, exposure to latest techniques. Topics include:
*Linux cluster computing

  • PBS systems
  • Parallel programming using MPI, OpenMP, pthreads
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This course will cover the techniques and topics that are widely used in real-world parallel computing. This is about learning how to run your programs on hundreds of computers or thousands of CPUs to solve real-world large problems. This is about how to handle large-scale data processing as those used in Google.

to:

This course will cover the techniques and topics that are widely used in real-world parallel computing. This is about learning how to run your programs on hundreds of computers or thousands of CPUs to solve real-world large problems. This is about how to handle large-scale data processing as those used in Google. Students in science and engineering will all benefit from this course as scientific computing has become one of the main ways for discovery and invention.

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Meeting Time(s): MWF 11:15AM-12:05PM\\

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MWF 11:15AM-12:05PM\\

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Course Meeting Time & Location Meeting Time(s): MWF 11:15AM-12:05PM
Classroom: 2A21 Swearinger Engineering Center

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Course Meeting Time & Location

Meeting Time(s): MWF 11:15AM-12:05PM
Classroom: 2A21 Swearinger Engineering Center

to:
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Course CSCE 569 Parallel Computing

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CSCE 569 Parallel Computing, Spring 2009

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This course will cover the techniques and topics that are widely used in real-world parallel computing.

to:

This course will cover the techniques and topics that are widely used in real-world parallel computing. This is about learning how to run your programs on hundreds of computers or thousands of CPUs to solve real-world large problems. This is about how to handle large-scale data processing as those used in Google.

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Blue-gene SuperComputer with 212992 CPUs

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Img:http://graphics8.nytimes.com/images/blogs/bits/posts/supercomputer.533.jpg

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http://graphics8.nytimes.com/images/blogs/bits/posts/supercomputer.533.jpg

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This course will cover the techniques and topics that are widely used in real-world parallel computing.

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This course will cover the techniques and topics that are widely used in real-world parallel computing.

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Img:http://graphics8.nytimes.com/images/blogs/bits/posts/supercomputer.533.jpg

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If you have problem to enroll, pls contact/call CSE secretary

Jewel T. Rogers 803 777-7849 (phone) Swearingen Bldg., Room 3A01L

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If you have problem to enroll, pls contact/call CSE secretary

Jewel T. Rogers 803 777-7849 (phone) Swearingen Bldg., Room 3A01L

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Course Description This course will cover the techniques and topics that are widely used in real-world parallel computing.

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Course Description This course will cover the techniques and topics that are widely used in real-world parallel computing.

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Course CSCE 822 - DATA MINING&WAREHOUSING

to:

Course CSCE 569 Parallel Computing

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Many interesting applications, Hands-on projects, exposure to techniques from machine learning, pattern recognization, statistics, mathematics, and computational intelligence

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Many interesting applications, Hands-on projects, exposure to techniques from

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Meeting Time(s): MW 4:00PM- 5:15PM\\

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Meeting Time(s): MWF 11:15AM-12:05PM\\

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You should be able to write some basic programs to do data format manipulation or implement some basic algorithms.

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You should be able to program using high-level language C/C++ or java.

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Easy acquision of huge amount of data in science, business, and national security makes it critial to extract informative knowledge and patterns from these data to ensure the competitiveness in the world. Data mining have been intensively used in large companies such as IBM, HP, Ebay, Wellsfargo, by govenmental organizations such as National Security Agency and CIA, and in the emerging fields of genomics or bioinformatics. Understanding principles of data mining and obtaining hands-on experience of implementing data mining projects will greatly improve the competitiveness of students in the job market as well as enhancing their research skills. This course will cover the techniques and topics that are widely used in real-world data mining including classification, clustering, dimension reduction, feature selection, frequent itemset mining, open-ended knowledge discovery, and etc. We will use real-world data (including bioinformatics) to challenge your skills of data mining. Students from computer science, engineering, biostatistics, (molecular) biology, medicine are all encouraged to enroll.

to:

This course will cover the techniques and topics that are widely used in real-world parallel computing.

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You should be able to write some basic programs to do data format manipulation and or implement some basic algorithms.

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You should be able to write some basic programs to do data format manipulation or implement some basic algorithms.

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You must be able to write programs to do data format manipulation and implement some basic algorithms.

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You should be able to write some basic programs to do data format manipulation and or implement some basic algorithms.

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Prerequisite You must be able to write programs to do data format manipulation and implement some basic algorithms.

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Easy acquision of huge amount of data in science, business, and national security makes it critial to extract informative knowledge and patterns from these data to ensure the competitiveness in the world. Data mining have been intensively used in large companies such as IBM, HP, Ebay, Wellsfargo, by govenmental organizations such as National Security Agency and CIA, and in the emerging field of genomics or bioinformatics. Understanding the principles of data mining and obtaining hands-on experience of implementing data mining projects will greatly improve the competitiveness of students in the job market as well as enhance their research skills. This course will cover the techniques and topics that are widely used in real-world data mining projects including classification, clustering, dimension reduction, feature selection, open-ended knowledge discovery, and etc. We will use real-world data (including bioinformatics) to challenge your skills of data mining. Students from computer science, engineering, biostatistics, (molecular) biology, medicine are all encouraged to enroll.

to:

Easy acquision of huge amount of data in science, business, and national security makes it critial to extract informative knowledge and patterns from these data to ensure the competitiveness in the world. Data mining have been intensively used in large companies such as IBM, HP, Ebay, Wellsfargo, by govenmental organizations such as National Security Agency and CIA, and in the emerging fields of genomics or bioinformatics. Understanding principles of data mining and obtaining hands-on experience of implementing data mining projects will greatly improve the competitiveness of students in the job market as well as enhancing their research skills. This course will cover the techniques and topics that are widely used in real-world data mining including classification, clustering, dimension reduction, feature selection, frequent itemset mining, open-ended knowledge discovery, and etc. We will use real-world data (including bioinformatics) to challenge your skills of data mining. Students from computer science, engineering, biostatistics, (molecular) biology, medicine are all encouraged to enroll.

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Easy acquision of huge amount of data in science, business, and national security makes it critial to extract informative knowledge and patterns from these data to ensure the competitiveness in the world. Data mining have been intensively used in large companies such as IBM, HP, Ebay, Wellsfargo, by govenmental organizations such as National Security Agency and CIA, and in the emerging field of genomics or bioinformatics. Understanding the principles of data mining and obtaining hands-on experience of implementing data mining projects will greatly improve the competitiveness of students in the job market as well as enhance their research skills. This course will cover the techniques and topics that are widely used in real-world data mining projects including classification, clustering, dimension reduction, feature selection, open-ended knowledge discovery, and etc. We will use real-world data (mostly from bioinformatics applications) to challenge your skills of data mining. Students from computer science, engineering, biostatistics, (molecular) biology, medicine are all encouraged to enroll.

to:

Easy acquision of huge amount of data in science, business, and national security makes it critial to extract informative knowledge and patterns from these data to ensure the competitiveness in the world. Data mining have been intensively used in large companies such as IBM, HP, Ebay, Wellsfargo, by govenmental organizations such as National Security Agency and CIA, and in the emerging field of genomics or bioinformatics. Understanding the principles of data mining and obtaining hands-on experience of implementing data mining projects will greatly improve the competitiveness of students in the job market as well as enhance their research skills. This course will cover the techniques and topics that are widely used in real-world data mining projects including classification, clustering, dimension reduction, feature selection, open-ended knowledge discovery, and etc. We will use real-world data (including bioinformatics) to challenge your skills of data mining. Students from computer science, engineering, biostatistics, (molecular) biology, medicine are all encouraged to enroll.

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Course Title: CSCE 822 - DATA MINING&WAREHOUSING

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Course Title: CSCE 822 - DATA MINING&WAREHOUSING

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Course Title: CSCE 822 - DATA MINING&WAREHOUSING

to:

Course Title: CSCE 822 - DATA MINING&WAREHOUSING

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Course Level: Graduate (CSCE822)

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Course Title: Data Mining Principles and Applications\\

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Course Title: CSCE 822 - DATA MINING&WAREHOUSING

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http://scigen.org/datamining/pub/skins/pmwiki/dmicon.jpg

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Classroom: 2A31 Swearinger Engineering Center

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Classroom: 2A21 Swearinger Engineering Center

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Machine Learning and Evolution Group (MLEG)\\

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Instructor: Dr. Jianjun Hu
Email: jianjunh AT cse.sc.edu
Office: 3A66 Swearinger Engineering Center
Office Hours: MW 1:30PM-3:30PM or by Appointment.

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Instructor: Dr. Jianjun Hu
Email: jianjunh AT cse.sc.edu
Office: 3A66 Swearinger Engineering Center
Office Hours: MW 1:30PM-3:30PM or by Appointment.

to:
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Course Description Easy acquision of huge amount of data in science, business, and national security makes it critial to extract informative knowledge and patterns from these data to ensure the competitiveness in the world. Data mining have been intensively used in large companies such as IBM, HP, Ebay, Wellsfargo, by govenmental organizations such as National Security Agency and CIA, and in the emerging field of genomics or bioinformatics. Understanding the principles of data mining and obtaining hands-on experience of implementing data mining projects will greatly improve the competitiveness of students in the job market as well as enhance their research skills. This course will cover the techniques and topics that are widely used in real-world data mining projects including classification, clustering, dimension reduction, feature selection, open-ended knowledge discovery, and etc. We will use real-world data (mostly from bioinformatics applications) to challenge your skills of data mining. Students from computer science, engineering, biostatistics, (molecular) biology, medicine are all encouraged to enroll.

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Course Description Easy acquision of huge amount of data in science, business, and national security makes it critial to extract informative knowledge and patterns from these data to ensure the competitiveness in the world. Data mining have been intensively used in large companies such as IBM, HP, Ebay, Wellsfargo, by govenmental organizations such as National Security Agency and CIA, and in the emerging field of genomics or bioinformatics. Understanding the principles of data mining and obtaining hands-on experience of implementing data mining projects will greatly improve the competitiveness of students in the job market as well as enhance their research skills. This course will cover the techniques and topics that are widely used in real-world data mining projects including classification, clustering, dimension reduction, feature selection, open-ended knowledge discovery, and etc. We will use real-world data (mostly from bioinformatics applications) to challenge your skills of data mining. Students from computer science, engineering, biostatistics, (molecular) biology, medicine are all encouraged to enroll.

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Course Title: CSCE 822 - DATA MINING&WAREHOUSING

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Course CSCE 822 - DATA MINING&WAREHOUSING

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Course Level: Undergraduate (CSCE590)/Graduate (CSCE822)

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Course Level: Graduate (CSCE822)