# Syllabus

**ECGR 6090/8090 - Multidimensional Stochastic Signal Processing (3)**

Catalog Data |
Prerequisites: ECGR 3111 or permission of Department. Review of probability, univariate and multivariate distribution functions, noise modeling, least-squares estimation, non-linear optimization, Markov chains, Bayes theorem; applications. (On demand) |

Lecture Times |
Monday/Wednesday 11:00 a.m. - 12:15 p.m. Office Hours Monday/Wednesday 10:00 a.m. - 11:00 a.m |

Lecture Room |
Woodward Hall 140 |

Reference |
The Elements of Statistical Learning: Data Mining, Inference and Prediction, Hastie, Tibshirani, & Friedman, 2nd Edition, Springer, 2009. |

Additional References |
Pattern Classification, Duda, Hart & Stork, John Wiley & Sons, 2001. |

Goals |
The objective of this course is to provide students with a working knowledge required to analyze and design learning algorithms and systems. |

Prerequisite |
Permission of the Department |

Class Topics |
Probability Theory Non-Linear Optimization Linear & Non-Linear Filtering |

Outcomes |
The following competencies should be imparted to the students: 1. An understanding of basic probability theory as applied in statistical learning contexts. 2. The ability to implement basic filtering and learning algorithms on data having 2 or more dimensions. 3. The ability to analyze non-linear systems and implement non-linear minimization algorithms. 4. Hands-on experience with various computer tools for implementing and testing multidimensional signal analysis systems through a variety of project. |

Computer Usage |
Students design, simulate, and analyze a variety of computer vision and pattern recognition projects. MATLAB should be accessible as a tool for completing projects. |

Laboratory |
Students use computer laboratories for the implementation of the design projects outside of class. |

Design Content |
The design projects for the course vary from semester to semester. |

Grading * |
There no two exams in this course. Grades are determined by performance on homeworks, projects, and in-class presentations. The weight of each item in determining the final grade is as follows: Homeworks/Quizzes 20% Reports 20% Examinations 30% |

Follow-up Courses |
N/A |

Academic Integrity |
Students have the responsibility to know and observe the requirements of the UNCC Code of Student Academic Integrity (2001-2003 UNCC Catalog, p. 275) . This code forbids cheating, fabrication or falsification of information, multiple submission of academic work, plagiarism, abuse of academic materials, and complicity in academic dishonesty. |

Notes |
Semester syllabus will be provided to the students on the first day of class. |

Coordinator |
A. Willis, Professor of Electrical & Computer Engineering |

Prepared by |
A. Willis, December 20, 2007 |

* Grading policy may be modified by the instructor for each section of the course.

* Grading scale : 90-100 A, 80-89 B, 70-79 C, with ``curve,'' if any, entirely at the discretion of the instructor. Quizzes may be given without warning.

Important Notes for Students

Students in this course seeking accommodations to disabilities must first consult with the Office of Disability Services and follow the instructions of that office for obtaining accommodations.

The use of cell phones, beepers, or other communication devices is disruptive, and is therefore prohibited during class. Except in emergencies, those using such devices must leave the classroom for the remainder of the class period.

Students are permitted to use computers during class for note-taking and other class-related work

only. Those using computers during class for work not related to that class must leave the classroom for the remainder of the class period.

Collaboration (not copying) on homework and computer projects is encouraged. However, students may NOT share program code or report material. You must read the textbook; it is impossible to cover all material during class.

Catalog Data |
The course builds upon and synthesizes knowledge from the engineering science, mathematics, and physical sciences stem of core curriculum. The specific topics teach engineering analysis, synthesis, and design, while simultaneously affording an opportunity for the students to investigate an area of specialization. May be repeated for credit. |

Lecture Times |
Friday 2:00 p.m. - 3:15 p.m. |

Lecture Room |
Woodward Hall 125 |

Reference |
Pattern Recognition and Machine Learning, Christopher Bishop, Springer, 2007. |

Additional References |
Pattern Classification, Duda, Hart & Stork, John Wiley & Sons, 2001. |

Goals |
The objective of this course is to provide students with a working knowledge required to analyze and design computer vision and pattern recognition algorithms and systems. |

Prerequisite |
Permission of the Department |

Class Topics |
Probability Theory |

Outcomes |
The following competencies should be imparted to the students: 1. An understanding of basic probability theory as applied in pattern recognition contexts (assessment by homework). 2. The ability to implement basic pattern recognition algorithms (assessment by projects and homework). 3. The ability to implement basic classification algorithms (assessment by projects and homework). 4. Hands-on experience with various computer tools for implementing and testing pattern recognition systems in a variety of projects (assessment by design project). |

Computer Usage |
Students design, simulate, and analyze a variety of computer vision and pattern recognition projects. MATLAB should be accessible as a tool for completing projects. |

Laboratory |
Students use computer laboratories for the implementation of the design projects outside of class. |

Design Content |
The design projects for the course vary from semester to semester. |

Grading * |
There no two exams in this course. Grades are determined by performance on homeworks, projects, and in-class presentations. The weight of each item in determining the final grade is as follows: |

Follow-up Courses |
N/A |

Academic Integrity |
Students have the responsibility to know and observe the requirements of the UNCC Code of Student Academic Integrity (2001-2003 UNCC Catalog, p. 275) . This code forbids cheating, fabrication or falsification of information, multiple submission of academic work, plagiarism, abuse of academic materials, and complicity in academic dishonesty. |

Notes |
Semester syllabus will be provided to the students on the first day of class. |

Coordinator |
A. Willis, Professor of Electrical & Computer Engineering |

Prepared by |
A. Willis, December 20, 2007 |

* Grading policy may be modified by the instructor for each section of the course.

* Grading scale : 90-100 A, 80-89 B, 70-79 C, with ``curve,'' if any, entirely at the discretion of the instructor. Quizzes may be given without warning.

Important Notes for Students

Students in this course seeking accommodations to disabilities must first consult with the Office of Disability Services and follow the instructions of that office for obtaining accommodations.

The use of cell phones, beepers, or other communication devices is disruptive, and is therefore prohibited during class. Except in emergencies, those using such devices must leave the classroom for the remainder of the class period.

Students are permitted to use computers during class for note-taking and other class-related work

only. Those using computers during class for work not related to that class must leave the classroom for the remainder of the class period.

Collaboration (not copying) on homework and computer projects is encouraged. However, students may NOT share program code or report material. You must read the textbook; it is impossible to cover all material during class.