Download Syllabus - UTEP Computer Science

CS 4365 Topic in Soft Computing: Machine Learning
CS 5354 Topic in Intelligent Computing: Machine Learning
Spring 2013
Instructor:
Olac Fuentes
Email: [email protected]
Web: www.cs.utep.edu/ofuentes
Office hours: Mondays and Wednesdays 2:00-3:30, or by appointment, in CCSB 3.0412 (feel free
to drop by at other times if my door is open).
Chat: [email protected]
Meeting times and place: MW 12:00-1:20 in CCSB 1.0204
Introduction:
Machine Learning studies the development of programs that can improve in the performance of a
task with experience. For many difficult problems, solutions based on machine learning
outperform all other solutions proposed to date. Examples of these problems include speech
recognition, classification of objects in images, weather prediction, fraud detection, robot
navigation, and many others.
In this course we will study several of the most commonly used machine learning algorithms and
their application to problems in several areas of interest. We will also discuss current research
issues in Machine Learning and each student will do a research project related to a problem of
his/her interest.
Course Contents:
1) Introduction (Chapters 1, 2, and 3)
a) Machine Learning and Data Mining
b) Examples
c) Input – What do we learn from?
2) Basic Methods (Chapter 4)
a) Statistical methods
b) Divide and conquer
c) Instance-based learning
d) Clustering
3) Evaluating results (Chapter 5)
a) Training and testing sets
b) Cross-validation
4) Learning Algorithms (Chapter 6)
a) Decision trees
b) Decision rules
c) Instance-based learning
d) Semi-supervised learning
e) Feed-forward neural networks
f) Support vector machines
5) Data Transformations (Chapter 7)
a) Principal Component analysis
b) Other methods
6) Ensembles of classifiers (Chapter 8)
a) Bagging
b) Randomization
c) Boosting
d) Stacking
e) Error-correcting output coding
7) Research Problems (Chapter 9)
a) Application
b) Big data
c) Data stream learning
d) Text mining
e) Web mining
Pre-requisites:
There are no formal prerequisites, but knowledge of programming, elementary calculus, linear
algebra, probability, and statistics is useful.
Grading:
Homework and programming projects 20%
2 partial exams 25%
Class participation and presentations 10%
Final Exam 20%
Final Project 25%
Bibliography:
Text:
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition. Ian H. Witten ,
Eibe Frank, and Mark A. Hall. The Morgan Kaufmann Series in Data Management Systems.
2011.
Others:
Machine Learning, Tom Mitchell, McGraw-Hill, 1998.
Introduction to Machine Learning, Ethem Alpaydin. Second Edition. MIT Press, 2009.
Recent research papers.
Tools:
WEKA (Waikato Environment for Knowledge Analysis)
Matlab