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January 12, 2021, at 12:57 AM EST by 76.213.117.150 -
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Office Hours: TTH 1:30-2:30am or by Appointment.

to:

Office Hours: TTH 1:20-2:20am or by Appointment.

January 12, 2021, at 12:55 AM EST by 76.213.117.150 -
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Classroom: Virtually online via zoom meeting. (Link will posted on dropbox.cse.sc.edu)

to:

Classroom: Virtually online via zoom meeting. (Link will posted at dropbox.cse.sc.edu)

January 12, 2021, at 12:47 AM EST by 76.213.117.150 -
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Classroom: 300 Main street, B110

to:

Classroom: Virtually online via zoom meeting. (Link will posted on dropbox.cse.sc.edu)

December 29, 2020, at 10:26 PM EST by 76.213.117.150 -
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CSCE 546 Mobile Application Development Spring 2020

to:

CSCE 546 Mobile Application Development Spring 2021

January 12, 2020, at 07:39 PM EST by 76.213.117.150 -
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January 12, 2020, at 07:39 PM EST by 76.213.117.150 -
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Office: 3A47 Swearinger Engineering Center\\

to:

Office: 2223 Storey Innovation Center, 550 Assembly St.\\

January 12, 2020, at 07:38 PM EST by 76.213.117.150 -
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CSCE 546 Mobile Application Development Spring 2019

to:

CSCE 546 Mobile Application Development Spring 2020

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Meeting Time: TTH 1:15PM-2:30PM \\

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Meeting Time: TTH 02:50 PM - 04:05 PM \\

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Classroom: 300 Main B110

Textbooks

Ionic in Action: Hybrid Mobile Apps with Ionic and AngularJS
ISBN-10: 1633430081; This textbook is optional.

to:

Classroom: 300 Main street, B110

Required Textbooks

Ionic 4+: Creating awesome apps for iOS, Android, Desktop and Web
ISBN-10: 3945102529;

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Office Hours: TTH 2:30-3:30am or by Appointment.

to:

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

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301 Main Street, Columbia, SC, 29201

to:

550 assembly street, Columbia, SC, 29201

January 15, 2019, at 08:31 AM EST by 10.31.4.131 -
Changed lines 86-87 from:

Swearingen Bldg., Room 3A01L

to:

Storey Innovation Center 2nd floor
550 assembly street, Columbia, SC

January 15, 2019, at 08:25 AM EST by 10.31.4.131 -
Changed lines 1-2 from:

CSCE 546 Mobile Application Development Spring 2018

to:

CSCE 546 Mobile Application Development Spring 2019

January 11, 2018, at 01:45 AM EST by 76.213.117.150 -
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CSCE 590 Mobile Application Development Spring 2017

to:

CSCE 546 Mobile Application Development Spring 2018

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Classroom: 2A19

to:

Classroom: 300 Main B110

March 20, 2017, at 09:44 PM EST by 76.213.117.150 -
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Barbara 803 777-7849 (phone)

to:

803 777-7849 (phone)

January 11, 2017, at 04:44 PM EST by 129.252.33.94 -
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Meeting Time: TTH 10:05AM-11:25AM \\

to:

Meeting Time: TTH 1:15PM-2:30PM \\

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Classroom: 300 Main St. B201

to:

Classroom: 2A19

January 11, 2017, at 04:43 PM EST by 129.252.33.94 -
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Meeting Time: TTH 4:30AM-5:40AM \\

to:

Meeting Time: TTH 10:05AM-11:25AM \\

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Classroom: SWGN 2A31

to:

Classroom: 300 Main St. B201

January 11, 2017, at 04:43 PM EST by 129.252.33.94 -
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https://elliebludigital.com/wp-content/uploads/2014/08/mobile-app-development.jpg

to:

http://www.veridic.in/blog/wp-content/uploads/2016/04/mobile-apps-825x510.png

January 11, 2017, at 04:42 PM EST by 129.252.33.94 -
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CSCE 590 Mobile Application Development Spring 2016

to:

CSCE 590 Mobile Application Development Spring 2017

February 16, 2016, at 10:03 AM EST by 129.252.33.87 -
Changed lines 27-31 from:

Office Hours: TTH 10:30-12:00am or by Appointment.

to:

Office Hours: TTH 2:30-3:30am or by Appointment.

January 11, 2016, at 02:31 PM EST by 129.252.33.75 -
Changed lines 27-31 from:

Office Hours: or by Appointment.

to:

Office Hours: TTH 10:30-12:00am or by Appointment.

January 11, 2016, at 02:30 PM EST by 129.252.33.75 -
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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.

to:

We are going to learn Hybrid Mobile application development using HTML5/CSS/Javascript. Why App? Why Hybrid App? Hybrid app is the fastest way to deploy cross-platform apps. You also learn the skills for web development too.

Changed lines 6-9 from:

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.

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

to:

Mobile Apps for consumers and enterprises.

https://elliebludigital.com/wp-content/uploads/2014/08/mobile-app-development.jpg

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Meeting Time: TTH 1:15 am - 2:30 am \\

to:

Meeting Time: TTH 4:30AM-5:40AM \\

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Classroom: SWGN 2A14

to:

Classroom: SWGN 2A31

Changed lines 20-22 from:

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

to:

Ionic in Action: Hybrid Mobile Apps with Ionic and AngularJS
ISBN-10: 1633430081; This textbook is optional.

Changed lines 27-31 from:

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

to:

Office Hours: or by Appointment.

Changed lines 33-34 from:

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.

to:

This course will cover the techniques and topics Hybrid App development using cordova. It will prepare you with skills for working in companies such as Google, Microsoft, Amazon, IBM, and many other business intelligence enterprises.

Changed lines 36-54 from:

Many interesting topics will be covered:

  1. Loops and lists
  2. Functions and branching
  3. Input data
  4. Arrays
  5. Files, strings, dictionaries
  6. Graphs, plotting, visualization
  7. Classes
  8. Random numbers
  9. Object-oriented programming
  10. Sequences and difference equation
  11. Discrete calculus
  12. Solving Differential equations
  13. parallel computing with python
to:

Many interesting topics may be covered:

  1. Overview of Mobile App development, Native Apps vs. Hybrid Apps, iOS/Android/Windows Platforms, #Java/Swift/Javascript+HTML5
  2. Application development fundamentals
  3. Programming fundamentals including computer programs, languages, compilers
  4. SDKs, development tools, running sample apps and simulating it on PC or on phones
  5. Handling basic interactions
  6. Creating user-interface components –text fields and buttons
  7. Link GUI elements to their functions
  8. Language fundamentals
  9. Data types, methods, messaging
  10. Final project proposal
  11. Getting familiar with user-interface components
  12. Views, Sliders, Switch, Image view, listview, table view
  13. Human Interface
  14. Creating a great user interface, using system-provided GUI controls.
  15. Collections and utility app
  16. Model-View-Controller (MVC) paradigm
  17. Collections to hold data
  18. Utility App to display multiple views
  19. Table and List views
  20. Displaying rows, deleting, inserting rows
  21. Application Settings and Data Persistence
  22. Exploring file system, reading data from file, creating and deleting files and directories, #writing data to files
  23. Cloud storage for app development
  24. iCloud or Parse framework as example framework
  25. Midterm
  26. GPS locations
  27. Using GPS location of the mobile devices
  28. Audio and Accelerometer
  29. Audio – play sounds - Vibration – force device to vibrate - Getting device orientation (x, y, z axes) - Getting raw accelerometer data - Filtering accelerometer data
  30. Gestural Inputs and Camera
  31. Pinch, Swipe, new gestures
  32. Mobile App testing
  33. Deploying Apps to different mobile app platforms
  34. Presentation of final project and project report
  35. No final exam (final project is used instead)
January 11, 2016, at 01:59 PM EST by 129.252.11.81 -
Changed lines 1-2 from:

CSCE 206 Scientific Application Programming Spring 2016

to:

CSCE 590 Mobile Application Development Spring 2016

January 10, 2016, at 09:33 PM EST by 24.40.214.249 -
Changed lines 1-2 from:

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 -
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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)

to:

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 -
Changed lines 26-30 from:

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 -
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 -
Changed lines 1-2 from:

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 -
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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 -
Changed lines 9-10 from:

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

to:
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 -
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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 -
Changed lines 6-8 from:

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)