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.

Learning Outcomes:

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:

Assessment Method:

The students will be closely monitored through personal communication, homeworks, computer exercises, exams and final projects to make sure that the outcomes are achieved as much as possible.