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November 30, 2018, at 09:13 AM EST by 10.31.3.137 -
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11a Deep Learning \\

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15 Combining Multiple Learners 351\\

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15 Ensemble Learning, Multiple Learners 351\\

November 29, 2018, at 11:46 PM EST by 76.213.117.150 -
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CSCE 883 Machine Learning Spring 2018

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CSCE 883 Machine Learning Fall 2018

November 29, 2018, at 11:36 PM EST by 76.213.117.150 -
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If you have problem to enroll, pls contact/call CSE secretary

Barbara 803 777-7849 (phone) Swearingen Bldg., Room 3A01L

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

803-777-6959 (phone) Storey Innovation Center, 550 Assembly street.

August 17, 2018, at 12:50 PM EST by 76.213.117.150 -
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Office: 3A66 Swearinger Engineering Center
Office Hours: MW 1:00PM-2:30PM or by Appointment.

to:

Office: 2223 Storey Innovation Center (500 Assembly street)
Office Hours: TTH 3:00PM-4:00PM or by Appointment via email.

August 17, 2018, at 12:47 PM EST by 76.213.117.150 -
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Meeting Time: TTH 12:30PM- 1:45PM
Classroom: 2A18 Swearinger Engineering Center

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Meeting Time: TTH 1:15PM- 2:30PM
Classroom: 2A05 Swearinger Engineering Center

April 18, 2018, at 08:29 AM EST by 129.252.33.21 -
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CSCE 883 Machine Learning Spring 2012

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CSCE 883 Machine Learning Spring 2018

April 18, 2018, at 08:22 AM EST by 129.252.33.21 -
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ISBN-10: 2nd edition (suggested). 026201243X (Feb 10, 2010) The MIT Press

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Publisher: The MIT Press; third edition edition (August 22, 2014) Language: English ISBN-10: 0262028182 ISBN-13: 978-0262028189

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

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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

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CSCE 883 Machine Learning Spring 2012

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

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

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Jewel T. Rogers 803 777-7849 (phone)

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Barbara 803 777-7849 (phone)

January 24, 2010, at 07:50 PM EST by 75.183.182.225 -
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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 -
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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

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Classroom: 2A24 Swearinger Engineering Center

December 21, 2009, at 05:10 PM EST by 129.252.11.239 -
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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 -
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Introduction to Machine Learning by Ethem Alpaydin

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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 -
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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 -
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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.
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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

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December 07, 2009, at 05:23 PM EST by 129.252.11.239 -
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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 -
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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\\

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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

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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)

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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.

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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 -
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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)

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'''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

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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.

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