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ECE 30010 - Introduction to Machine Learning and Pattern Recognition

Lecture Hours: 3 Credits: 3

Professional Attributes
EE Special Content

This is a Special Content course. No more than 6 credits of Special Content type courses may apply towards the ECE Requirements of the BSEE. Excess hours can be used for Unrestricted Electives.

Normally Offered: Maymester

Requisites:
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)

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, 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.

Supplementary Information:
Course is offered during maymester session. Students interested in this course must register for the Study Abroad course number. See Professor Ersoy for more information.

Required Text(s):
  1. Machine Learning, An Algorithmic Perspective, Stephen Marshall, Chapman & Hall / CRC Press, 2009, ISBN No. 978-1-4200-6718-7.
Recommended Text(s):
  1. 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.
  2. Unpublished course notes by the instructor, Okan Ersoy.

Learning Objectives:

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]
Assessment Method for Learning Objectives: The students will be closely monitored through personal communication, homework, computer exercises, exams and final projects to make sure that the outcomes are achieved.

Lecture Outline:

Major Topics
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
6 Software implementations
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:

Establishment of Objectives and Criteria
Synthesis
Analysis
Construction
Testing

Engineering Design Consideration(s):

Economic
Environmental
Ethical
Health/Safety
Manufacturability
Social