ECE 49500 - Introduction to Machine Learning and Pattern Recognition

Lecture Hours: 3 Credits: 3

Counts as:

Experimental Course Offered: Maymester 2009, Maymester 2010

Catalog Description:
Intelligent information processing, search and retrieval, classification, recognition, prediction and optimization with machine learning and pattern recognition algorithms such as neural networks, support vector machines, decision trees and data mining methods, current models and architectures, implementational topics especially in software, applications in areas such as information processing, search and retrieval of internet data, forecasting (prediction), classification, signal/image processing, pattern recognition, optimization, simulation, system identification, communications, control, bioinformatics, bioengineering, management and finance. Topics covered will also be illustrated with software packages MATLAB, related toolboxes and WEKA.

Required Text(s):
  1. Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Ian H. Witten, Eibe Rank, Morgan Kaufmann, 2005, ISBN No. 0-12-088407-0.

Recommended Text(s): None.

Learning Outcomes:

A student who successfully fulfills the course requirements will have demonstrated:
  1. an ability to apply knowledge of mathematics, science, and engineering. [a]
  2. an ability to design and conduct experiments, as well as to analyze and interpret data. [b]
  3. an ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability. [c]
  4. an ability to function on multi-disciplinary teams. [d]
  5. an ability to identify, formulate, and solve engineering problems. [e]
  6. an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. [f]
  7. the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context. [h]

Lecture Outline:

Machine learning and pattern recognition: introduction and examples
Input: concepts, representation and examples
Output: knowledge representation, decision trees and clusters
Algorithms: the basic methods with examples
Techniques to increase performance
Software implementations
Input and output transformations
Examples of real world applications
MATLAB: a software tool, associated toolboxes and examples of use
WEKA: another software tool and examples of use

Engineering Design Content:

Establishment of Objectives and Criteria

Engineering Design Consideration(s):