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December 03, 2019, at 07:15 AM EST by 129.252.11.40 -
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You should be able to program using high-level language C/C++ or java.

to:

Basic calculus required.

August 27, 2019, at 06:59 AM EST by 10.31.21.190 -
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A Primer on Scientific Programming with Python by Hans Petter Langtangen (2014)
ISBN-10: 3642549586 Springer;

to:

A Primer on Scientific Programming with Python by Hans Petter Langtangen (2016)
ISBN-10:3662498863 Springer;

August 21, 2019, at 09:45 PM EST by 76.213.117.150 -
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CSCE 206 Scientific Application Programming Spring 2018

to:

CSCE 206 Scientific Application Programming Fall 2019

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Meeting Time: TTH 10:0 am - 11:20 am
Classroom: SWRG 2A14

to:

Meeting Time: TTH 10:05 am - 11:20 am
Classroom: Horizon Parking Garage 210

January 11, 2018, at 12:49 AM EST by 76.213.117.150 -
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CSCE 206 Scientific Application Programming Fall 2017

to:

CSCE 206 Scientific Application Programming Spring 2018

Changed lines 14-16 from:

Meeting Time: TTH 10:05 am - 11:25 am
Classroom: Horizon Parking Garage 210

to:

Meeting Time: TTH 10:0 am - 11:20 am
Classroom: SWRG 2A14

Changed lines 25-30 from:

Office: 3A47 Swearinger Engineering Center
Office Hours: TTH 1:00PM-2:30PM or by Appointment.

to:

Office: 2223 Storey Innovation Center
Office Hours: TTH 1:00PM-2:00PM or by Appointment.

December 03, 2017, at 07:46 PM EST by 76.213.117.150 -
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Office Hours: TTH 3:0AM-4:00AM or by Appointment.

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

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.

to:

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

Changed lines 14-15 from:

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 -
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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 -
Changed lines 1-2 from:

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

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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.

to:

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

to:

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\\

to:

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 -
Changed lines 9-10 from:
to:

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

June 15, 2013, at 01:42 AM EST by 129.252.28.17 -
Changed lines 9-10 from:

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

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June 15, 2013, at 01:42 AM EST by 129.252.28.17 -
Changed lines 9-10 from:
to:

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

June 15, 2013, at 01:41 AM EST by 129.252.28.17 -
Changed lines 8-9 from:

to:

http://matplotlib.org/_images/tricontour_demo_001.png

June 15, 2013, at 01:40 AM EST by 129.252.28.17 -
Changed lines 8-9 from:
to:

June 15, 2013, at 12:52 AM EST by 129.252.28.17 -
Changed lines 31-32 from:

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 -
Changed line 13 from:

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 -
Changed lines 15-16 from:

Classroom: 2A24 Swearinger Engineering Center

to:

Classroom: 2A18 Swearinger Engineering Center

January 08, 2012, at 06:38 PM EST by 129.252.11.94 -
Changed lines 1-2 from:

CSCE 883 Machine Learning Spring 2010

to:

CSCE 883 Machine Learning Spring 2012

Changed line 14 from:

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 -
Changed lines 26-30 from:

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 -
Changed lines 17-19 from:

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 -
Changed lines 17-19 from:

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 -
Changed lines 66-82 from:

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 -
Changed line 9 from:

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 -
Changed lines 32-45 from:
  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 -
Changed lines 1-4 from:

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.