Relevant Courses
Courses relevant to our work.
- CSCE 5200: Information Retrieval and Web Search - Covers traditional material and
recent advances in information retrieval, study of indexing, processing and querying
textual data, basic retrieval models, algorithms and information retrieval system
implementations. Covers advanced topics in intelligent information retrieval, including
natural language processing techniques and smart web agents.
- CSCE 5210: Artificial Intelligence - Advanced study of issues relevant in the design
of intelligent computer systems. Topics included in this course are search techniques,
knowledge representation, issues in natural language processing and the design of
expert systems.
- CSCE 5215: Machine Learning - Theory and practice of machine learning. Decision trees,
neural network learning. Statistical learning methods, genetic algorithms, Bayesian
learning methods, rule-based learning and reinforcement learning. Improved learning
through bagging, boosting, and ensemble learning. Practical applications of machine
learning algorithms.
- CSCE 5216: Pattern Recognition - Study of the fundamentals of pattern recognition
techniques including Bayesian decision and estimation, non-parametric methods, multi-class
classifiers and feature selection methods.
- CSCE 5270: Computer-Human Interfaces - Emphasizes human performance in using computer
and information systems. Topics for software psychology include programming languages,
operating systems control languages, database query facilities, computer-assisted
dialogues, personal computing systems, editors, word processing and terminal usage
by non-skilled users.
- CSCE 5290: Natural Language Processing - Introduction to natural language processing;
modern theories of syntax; context-free parsing; transformational syntax and parsing;
augmented transition networks; and survey of natural language processing systems.
- CSCE 5380: Data-Mining - Introduction to data mining which includes main data mining
tasks, e.g. classification, clustering, association rules, and outlier detection,
and some of the latest developments, e.g. mining spatial data and web data.
- CSCE 6260: Advanced Pattern Recognition & Image Processing - Research and study of
specific problems and advanced topics, including the principles and pragmatics of
pattern recognition, digital image processing and analysis, and computer vision.
- CSCE 6280: Advanced Artificial Intelligence - Current research issues and advanced
topics involving both the principles and pragmatics within the area of artificial
intelligence. Topics include, but are not limited to, knowledge representation, intelligent
tutoring systems and semantic representation in natural language processing.
- CSCE 6290: Advanced Topics in Human/Machine Intelligence - Current topics in human/machine
intelligence such as advanced research topics in machine learning, natural language
processing, cognitive science, robot perception and intelligence, computer vision,
intelligent systems, expert systems, data mining, and human-centered computing. May
be repeated for credit when topics vary.
Includes:
- Dialogue Systems
- Semi-Supervised and Active Learning
- Cognitive Science
- Social Network Analysis
- CSCE 2900: Special Problems in Computer Science & Engineering - Individualized instruction
in theoretical or experimental problems. For elective credit only.
- CSCE 2996: Honors College Mentored Research Experience – Research experience conducted
by a freshman or sophomore honors student under the supervision of a faculty member.
May be taken only once for Honors College credit.
- CSCE 3220: Human Computer Interfaces - Human-Computer Interaction (HCI). Methods for
designing, prototyping, and evaluating user interfaces for computing applications.
Human capabilities, interface technology, interface design methods, and interface
evaluation tools and techniques.
- CSCE 3996: Honors College Mentored Research Experience - Research experience conducted
by an honors student with at least junior standing under the supervision of a faculty
member. May only be taken once for Honors College credit.
- CSCE 4110: Algorithms - Algorithm design methodologies, sorting, graph algorithms,
dynamic programming, backtracking, string searching and pattern matching.
- CSCE 4115: Formal Languages, Automata, and Computability - Introduces students to
the formal language theory that underlies modern computer science. Topics include
different representational forms for regular languages, context-free grammars, pushdown
automata, pumping lemmas for regular and context-free languages, and Chomsky's hierarchy.
- CSCE 4310: Introduction to Artificial Intelligence - Introduction to concepts and
ideas in artificial intelligence. Topics include search techniques, knowledge representation,
control strategies and advanced problem-solving architecture.
- CSCE 4890: Directed Study - Study by individuals or small groups if faculty supervisor
agrees. A plan of study approved by the faculty supervisor along with the study will
be graded by the faculty supervisor must be approved.
- CSCE 4930: Topics in Computer Science & Engineering - Topics vary. May be repeated
for credit.
- CSCE 4940: Special Computer Application Problem - Study defined by the student in
applying computer science to another field. Work supervised and work plan approved
by one faculty member from computer science and one from relevant application area;
one to three students may work together if all faculty advisers concerned agree. Open
to advanced undergraduate students capable of developing problems independently. May
be repeated for credit.
- CSCE 4950: Special Problems in Computer Science & Engineering - Prior approval of
plan of study by faculty supervisor.
- CSCE 4951: Honors College Capstone Thesis - Major research project prepared by the
student under the supervision of a faculty member and presented in standard thesis
format. An oral defense is required of each student for successful completion of the
thesis. May be substituted for HNRS 4000.
- CSCE 4999: Senior Thesis - Intended to be a serious exercise in the organization and
presentation of written material. Students select their own topics in consultation
with their faculty advisor. The thesis is a research paper and students are responsible,
with the advice of their faculty, for the investigation of sources, the accumulation
of data, the selection of pertinent material, and the preparation of the thesis in
acceptable form. Students must submit their own topics for thesis, with designated
advisor approval, before they are allowed to register for the course.
Graduate Admissions
We are always looking for new promising students that are interested in related topics
and are willing to work hard. Contact us to do research with the lab. See the UNT
Catalog and the CSE Department website to apply for Graduate Admissions.
New graduate students start studies each Fall semester. We recommend starting the
application process early and contacting us early in the process. It's a good idea
to start by reviewing the material online of our lab, our department, our college,
and our university to get a good introduction of what we offer at UNT. If you are
in the area, UNT offers tours on main campus as well as the Engineering Department
at Discovery Park. Feel free to contact us if you are interested in applying for research
in the lab.
Masters and PhD Tracks
UNT Computer Science department offers a PhD track for students already with a masters,
as well as, a track directly from a Bachelors. If you are interested in obtaining
a PhD, we recommend applying directly to the PhD program. For those only interested
in a Masters, the department also offers a Masters Program.
For PhD students, we will build a custom degree plan to achieve your academic goals.
For Masters students, there are several tracks to be used as guidelines to help achieve
courses in the time frame needed to complete the degree.
Master Student Tracks related to HiLT Lab:
- Artificial Intelligence
- Computer Vision and Intelligent Systems
- Data Management & Analysis
- Informatics