Relevant Courses

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.
o Dialogue Systems
o Semi-Supervised and Active Learning
o Cognitive Science
o 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.