ECE 39595 - Introduction to Machine Learning and Pattern RecognitionLecture Hours: 3 Credits: 3
Experimental Course Offered: Maymester 2011
MA 26100 or MA 26500
Requisites by Topic:
calculus and introductory linear algebra (Math 26100 and/or 26500 or equivalents with permission of the instructor)
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, implementation 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, management and finance. Topics covered will also be illustrated with the software package MATLAB and related toolboxes.
- Machine Learning, An Algorithmic Perspective, Stephen Marshall, Chapman & Hall / CRC Press, 2009, ISBN No. 978-1-4200-6718-7.
- Data Mining: Practical Machine Learning Tools and Techniques,, 2nd Edition, Ian H. Witten and Eibe Frank, Morgan Kaufmann Publishers, 2005, ISBN No. 0-12-088407-0.
- Unpublished course notes by the instructor, Okan Ersoy.
|1||Machine learning and pattern recognition: introduction and examples|
|2||Input: concepts, representation and examples|
|3||Output: knowledge representation, decision trees and clusters|
|4||Algorithms: the basic methods with examples|
|5||Techniques to increase performance|
|7||Input and output transformations|
|8||Examples of real world applications|
|9||MATLAB: a software tool, associated toolboxes and examples of use|
|10||WEKA: another software tool and examples of use|
|11||Python: an open-source software platform with similarities to Matlab|
Engineering Design Content:
Engineering Design Consideration(s):