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.

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.

Department of Computer Science and Engineering
University of South Carolina

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

Textbooks

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

Instructor: Dr. Jianjun Hu
Machine Learning and Evolution Laboratory (MLEG)
Email: jianjunh AT cse.sc.edu
Office: 3A66 Swearinger Engineering Center
Office Hours: MW 1:00PM-2:30PM or by Appointment.

Course Description 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.

Course Highlights Many interesting topics will be covered:

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

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

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

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

Department of Computer Science and Engineering
College of Engineering and Computing
University of South Carolina
301 Main Street, Columbia, SC, 29201

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