## Main.HomePage History

Hide minor edits - Show changes to markup

11a Deep Learning \\

15 Combining Multiple Learners 351\\

15 Ensemble Learning, Multiple Learners 351\\

*CSCE 883 Machine Learning Spring 2018*

*CSCE 883 Machine Learning Fall 2018*

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

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

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

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

Office: 3A66 Swearinger Engineering Center

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

Office: 2223 Storey Innovation Center (500 Assembly street)

Office Hours: TTH 3:00PM-4:00PM or by Appointment via email.

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

Classroom: 2A18 Swearinger Engineering Center

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

Classroom: 2A05 Swearinger Engineering Center

*CSCE 883 Machine Learning Spring 2012*

*CSCE 883 Machine Learning Spring 2018*

ISBN-10: 2nd edition (suggested). 026201243X (Feb 10, 2010) The MIT Press

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

Classroom: 2A24 Swearinger Engineering Center

Classroom: 2A18 Swearinger Engineering Center

*CSCE 883 Machine Learning Spring 2010*

*CSCE 883 Machine Learning Spring 2012*

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

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

Jewel T. Rogers 803 777-7849 (phone)

Barbara 803 777-7849 (phone)

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

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

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

Classroom: 2A21 Swearinger Engineering Center

Classroom: 2A24 Swearinger Engineering Center

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.

Introduction to Machine Learning by Ethem Alpaydin

Introduction to Machine Learning by Ethem Alpaydin\\

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

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

ISBN-10: 2nd edition (suggested). 026201243X (Feb 10, 2010) The MIT Press

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

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

Older version(2004): ISBN 0262012111

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

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

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

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

## 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.

## Blue-gene SuperComputer with 212992 CPUs

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

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

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

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.

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

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

- Introduction
- Fundamentals of the Analysis of Algorithm Efficiency
- Brute Force
- Divide-and-Conquer, Decrease and Conquer, Transform and Conquer
- Space and Time Tradeoffs
- Dynamic Programming
- Greedy Technique
- Limitation of Algorithm Power and Coping with the Limitations of Algorithm Power

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

*CSCE 350 Data Structure and Algorithms 2009*

*CSCE 883 Machine Learning Spring 2010*

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

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

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.

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.

Machine Learning and Evolution Group (MLEG)\\

Machine Learning and Evolution Laboratory (MLEG)\\

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.

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.

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

Many interesting topics will be covered:

- Introduction
- Fundamentals of the Analysis of Algorithm Efficiency
- Brute Force
- Divide-and-Conquer, Decrease and Conquer, Transform and Conquer
- Space and Time Tradeoffs
- Dynamic Programming
- Greedy Technique
- Limitation of Algorithm Power and Coping with the Limitations of Algorithm Power

Hardcover: 544 pages

Publisher: McGraw-Hill Science/Engineering/Math; 1 edition (June 5, 2003)

Language: English

ISBN-10: 0072822562

ISBN-13: 978-0072822564

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

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

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

*CSCE 569 Parallel Computing, Spring 2009*

*CSCE 350 Data Structure and Algorithms 2009*

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

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

dd

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

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)

dd

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

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

Office Hours: MW 1:30PM-3:30PM or by Appointment.

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