Faculty Profile
Rodney Nielsen
Title
Associate Professor
Department
Computer Science and Engineering
College
College of Engineering
-
Discovery Park BuildingF246
- Rodney.Nielsen@unt.edu
- Google Scholar Profile
- Rodney Nielsen - Home
- Human/Machine Intelligence and Language Technologies Lab
Curriculum Vitae
Education
PhD, University of Colorado at Boulder, 2008.
Major: Cognitive Science
PhD, University of Colorado at Boulder, 2008.
Major: Computer Science
MS, University of Colorado at Boulder, 2005.
Major: Computer Science
BA, Minot State University, 1986.
Major: Mathematics
BS, Minot State University, 1986.
Major: Computer Science
Current Scheduled Teaching*
CSCE 6950.912, Doctoral Dissertation, Spring 2020
CSCE 6940.912, Individual Research, Spring 2020
CSCE 6940.912, Individual Research, Spring 2020
CSCE 4201.001, Introduction to Artificial Intelligence, Spring 2020 Syllabus
INFO 6660.007, Readings in Information Science, Spring 2020
* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.
Previous Scheduled Teaching*
CSCE 6950.812, Doctoral Dissertation, Fall 2019
CSCE 6940.812, Individual Research, Fall 2019
CSCE 2900.812, Special Problems in Computer Science and Engineering, Fall 2019
CSCE 4950.812, Special Problems in Computer Science and Engineering, Fall 2019
CSCE 6940.812, Individual Research, Summer 10W 2019
CSCE 4950.812, Special Problems in Computer Science and Engineering, Summer 10W 2019
CSCE 5210.001, Artificial Intelligence, Spring 2019 SPOT
CSCE 6950.812, Doctoral Dissertation, Spring 2019
CSCE 6940.812, Individual Research, Spring 2019
CSCE 6940.812, Individual Research, Spring 8W2 2019
CSCE 2900.812, Special Problems in Computer Science and Engineering, Spring 2019
CSCE 4950.812, Special Problems in Computer Science and Engineering, Spring 2019
CSCE 6950.812, Doctoral Dissertation, Fall 2018
CSCE 6940.812, Individual Research, Fall 2018
CSCE 5215.001, Machine Learning, Fall 2018 SPOT
CSCE 2900.812, Special Problems in Computer Science and Engineering, Fall 2018
CSCE 4950.812, Special Problems in Computer Science and Engineering, Fall 2018
CSCE 6950.812, Doctoral Dissertation, Summer 10W 2018
CSCE 2900.812, Special Problems in Computer Science and Engineering, Summer 10W 2018
CSCE 5210.001, Artificial Intelligence, Spring 2018 SPOT
CSCE 5934.812, Directed Study, Spring 2018
CSCE 6950.812, Doctoral Dissertation, Spring 2018
CSCE 6940.812, Individual Research, Spring 2018
CSCE 5300.501, Introduction to Big Data and Data Science, Spring 8W2 2018
CSCE 5215.501, Machine Learning, Spring 8W1 2018
CSCE 2900.812, Special Problems in Computer Science and Engineering, Spring 2018
CSCE 6950.812, Doctoral Dissertation, Fall 2017
CSCE 6940.812, Individual Research, Fall 2017
CSCE 5215.001, Machine Learning, Fall 2017 SPOT
CSCE 5215.600, Machine Learning, Fall 2017 SPOT
CSCE 5950.812, Master's Thesis, Fall 2017
CSCE 2900.812, Special Problems in Computer Science and Engineering, Fall 2017
CSCE 2900.812, Special Problems in Computer Science and Engineering, Summer 10W 2017
CSCE 5210.001, Artificial Intelligence, Spring 2017 SPOT
CSCE 6950.812, Doctoral Dissertation, Spring 2017
CSCE 5950.812, Master's Thesis, Spring 2017
CSCE 2900.812, Special Problems in Computer Science and Engineering, Spring 2017
CSCE 5934.812, Directed Study, Fall 2016
CSCE 6950.812, Doctoral Dissertation, Fall 2016
CSCE 6940.812, Individual Research, Fall 2016
CSCE 5950.812, Master's Thesis, Fall 2016
CSCE 5933.001, Topics in Computer Science and Engineering, Fall 2016 SPOT
CSCE 6950.812, Doctoral Dissertation, Summer 10W 2016
CSCE 6940.812, Individual Research, Summer 10W 2016
CSCE 5950.812, Master's Thesis, Summer 5W2 2016
CSCE 2900.812, Special Problems in Computer Science and Engineering, Summer 10W 2016
CSCE 6950.812, Doctoral Dissertation, Spring 2016
CSCE 6940.812, Individual Research, Spring 2016
CSCE 5950.812, Master's Thesis, Spring 2016
CSCE 5380.001, Data Mining, Fall 2015 SPOT
CSCE 5934.812, Directed Study, Fall 2015
CSCE 6950.812, Doctoral Dissertation, Fall 2015
CSCE 6940.812, Individual Research, Fall 2015
CSCE 5950.812, Master's Thesis, Fall 2015
CSCE 4999.812, Senior Thesis, Fall 2015
CSCE 2900.812, Special Problems in Computer Science and Engineering, Summer 10W 2015
CSCE 6290.001, Advanced Topics in Human/Machine Intelligence, Spring 2015
CSCE 5934.812, Directed Study, Spring 2015
CSCE 6950.812, Doctoral Dissertation, Spring 2015
CSCE 6940.812, Individual Research, Spring 2015
CSCE 5950.812, Master's Thesis, Spring 2015
CSCE 6290.001, Advanced Topics in Human/Machine Intelligence, Fall 2014
CSCE 6940.812, Individual Research, Fall 2014
CSCE 5950.812, Master's Thesis, Fall 2014
CSCE 5934.812, Directed Study, Summer 10W 2014
CSCE 2900.812, Special Problems in Computer Science and Engineering, Summer 10W 2014
CSCE 5934.812, Directed Study, Spring 2014
CSCE 6940.812, Individual Research, Spring 2014
CSCE 5950.812, Master's Thesis, Spring 2014
CSCE 2900.812, Special Problems in Computer Science and Engineering, Spring 2014
CSCE 6933.003, Advanced Topics in Computer Science and Engineering, Fall 2013
CSCE 1040.002, Computer Science II, Fall 2013 Syllabus
CSCE 6940.812, Individual Research, Fall 2013
CSCE 6933.006, Advanced Topics in Computer Science and Engineering, Spring 2013
CSCE 4890.812, Directed Study, Spring 2013
CSCE 6940.812, Individual Research, Spring 2013
CSCE 5290.001, Natural Language Processing, Fall 2012
* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.
| Overall Summative Rating |
Challenge and Engagement Index |
Response Rate |
|---|---|---|
out of 5 |
out of 7 |
% of students responded |
- Overall Summative Rating (median):
This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):- The course as a whole was
- The course content was
- The instructor’s contribution to the course was
- The instructor’s effectiveness in teaching the subject matter was
- Challenge and Engagement Index:
This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):- Do you expect your grade in this course to be
- The intellectual challenge presented was
- The amount of effort you put into this course was
- The amount of effort to succeed in this course was
- Your involvement in course (doing assignments, attending classes, etc.) was