CAP: Computer Applications (For Computer Scientists) Courses

Courses

CAP 4053   AI Programming for Intelligent Environments

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: COP 3530

Introduction to the use of AI methods and programming for the development of intelligent systems, including game AI systems, robotic applications, and educational environments. Students will identify an appropriate AI project topic of interest to them, and work individually or as teams to design, develop, and evaluate an AI system for that topic.

CAP 4136   Malware Analysis

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: CDA 3101

This course covers software reverse engineering of executable code (or malware) to determine its function and affects or to recover the source code implementation.

CAP 4138   Reverse Software Engineering - Malware Analysis

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: CDA 3101C

This course covers software reverse engineering of executable code (or malware) to determine its function and affects or to recover the source code implementation.

CAP 4601   Artificial Intelligence

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: COP 3411 OR COP 3530

Introduction to Artificial Intelligence principles and techniques. Students will learn about core AI techniques for solving complex problems, including search strategies, knowledge-based techniques, and agent-based systems. Overview of AI topics such as intelligent agents, machine learning, as well as AI applications.

CAP 4770   Data Mining

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: COP 4710

Exposes students to data mining concepts and techniques and different data mining software. Covers data pre-processing and cleaning, concept hierarchy generation, attribute relevance analysis, association rule mining, classification algorithms, and cluster analysis. Offered concurrently with CAP 5771; graduate students will be assigned additional work.

CAP 4786   Introduction to Big Data Analytics

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: ((CAP 4710 AND STA 4321)) AND (COP 3530 OR COP 3022)

This course introduces students to the handling of Big Data on Hadoop's MapReduce environment. Students also learn Spark architecture and programming with the aim of doing big data analytics with machine learning algorithms in Spark.

CAP 4905   Directed Study

College of Sci and Engineering, Department of Computer Science

1-12 sh (may be repeated indefinitely for credit)

CAP 5600   Introduction to Artificial Intelligence

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)

Introduction to basic Artificial Intelligence theories and methods for solving complex and difficult problems using computers; goal- oriented procedures, search problems, knowledge representation and machine learning. Topics will include intelligent systems such as expert systems, intelligent agents and robots. Will be conducted within a cognitive science framework.

CAP 5771   Data Mining

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: COP 5725

Exposes students to data mining concepts and techniques and different data mining software. Covers data pre-processing and cleaning, concept hierarchy generation, attribute relevance analysis, association rule mining, classification algorithms, and cluster analysis. Offered concurrently with CAP4770.

CAP 5905   Directed Study

College of Sci and Engineering, Department of Computer Science

1-12 sh (may be repeated indefinitely for credit)

CAP 6579   Advanced Data Mining

College of Sci and Engineering, Department of Intelligent Systems & Robotics

3 sh (may not be repeated for credit)

This course will cover advanced topics in data mining on high dimensional data, including advanced feature selection techniques, advanced pattern mining, similarity searches (including minwise hashing and locality sensitive hashing), advanced classification methods, advanced cluster analysis, mining data streams, mining social networks, tree/graph mining, and privacy-preserving issues in data mining. Students are expected to have a course in data mining before taking this course.

CAP 6667   Advanced Topics in Intelligent Systems & Robotics

College of Sci and Engineering, Department of Intelligent Systems & Robotics

3 sh (may not be repeated for credit)
Prerequisite: EEE 6730

This seminar-style course provides doctoral students with an overview of trends in Intelligent Systems and Robotics and prepares them to conduct independent research in the field. Permission of the Instructor is required.

CAP 6671   Intelligent Agents

College of Sci and Engineering, Department of Intelligent Systems & Robotics

3 sh (may not be repeated for credit)

The course will cover the underlying theory of intelligent agents, both software agents and embodied agents, their implementation, and applications of single and multi-agent systems. The course will address common agent architectures and various methods of agent cooperation. The course will also explore how a range of other Artificial Intelligence techniques such as knowledge representation, reasoning, machine learning, planning, ontologies, and natural language interaction are leveraged by agents. Students will construct their own agents in order to solve a range of problems. The course will employ simulations of multi-agent systems involving both cooperating and competing agents. Students are expected to have a background with computer networks.

CAP 6772   Data Warehousing

College of Sci and Engineering, Department of Computer Science

3 sh (may not be repeated for credit)
Prerequisite: COP 5725

The primary focus of this course is on Data Warehousing and its applications to business intelligence. Some areas of concentration are: requirements gathering for data warehousing; data warehouse architecture; dimensional model design for data warehousing; physical database design for data warehousing; extracting, transforming, and loading strategies; introduction to business intelligence; design and development of business intelligence applications; expansion and support of a data warehouse. Prerequisites: COP5725, minimum grade of C.

CAP 6905   Directed Study

College of Sci and Engineering, Department of Computer Science

1-12 sh (may be repeated indefinitely for credit)

CAP 7640   Topics in Natural Language Processing

College of Sci and Engineering, Department of Intelligent Systems & Robotics

3 sh (may not be repeated for credit)

This course covers fundamental concepts in processing natural language text. It provides an in-depth examination of state-of-the-art knowledge-based and statistical methods to process unstructured text, perform word and sentence-level syntactic and semantic analysis, and build machine representations to perform different natural language tasks. The course covers a variety of applications of these methods including syntactic parsing, word sense disambiguation, text classification, information extraction, text summarization, language generation, language translation, and dialogue systems. Students taking this course are expected to have a background in computer programming and mathematical statistics. Successful completion of coursework is necessary to enroll in the dissertation.