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Course Criteria
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3.00 Credits
Required of students pursuing a Computer Science degree or emphasis, and open to other interested students. Covers 2-D and 3-D model creation, transformation, and various rendering techniques through completion of programming assignments. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: At the successful conclusion of this course, students will be able to: 1. Program 2D and 3D graphics applications using an appropriate API such as OpenGL. 2. Design software that is highly user interactive. 3. Apply modeling, rendering and animation techniques to a variety of problems. 4. Design lighting systems that use the 3D Phong shading model. 5. Render high quality images using texture mapping, ray tracing, and shadows. 6. Program Curves and Surfaces for use in CAD. 7. Program automated systems using Physically Based Modeling. Course fee required. Prerequisites: CS 2420 (Grade C or higher); AND CS 3005 (Grade C or higher). SP
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3.00 Credits
Required of students pursuing a Computer Science degree or emphasis. Introduces the broad field of artificial intelligence in computer software followed by specific applications in computer gaming strategies. Students will complete programming assignments. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Construct solutions for a range of problems using search algorithms. 2. Infer problems that can be solved using propositional logic and build appropriate solutions. 3. Design and implement Bayesian networks. 4. Integrate third-party libraries into solutions for large software projects. 5. Collaborate to solve large and complex problems. Course fee required. Prerequisites: CS 2420 (Grade C or higher); AND CS 2810 (Grade C or higher); AND CS 3005 (Grade C or higher). FA
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3.00 Credits
An introduction to database systems. Topics include the design and implementation of relational databases (B-trees, indexing, query planning and execution, transactions, ACID semantics) as well as data modeling and querying for applications using databases. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Design and implement the major components of a database management system. 2. Analyze the storage needs of a software project and create a data schema and query strategy to address those needs. 3. Construct substantial software that balances requirements of data safety, performance, and complexity. 4. Debate and criticize modern approaches to data management. Course fee required. Prerequisites: CS 2420 (Grade C or higher) AND CS 2810 (Grade C or higher). SP
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3.00 Credits
This course covers the database architecture and environment. Students will be able to manage user access control. Students will be able to perform backup, restore, and recovery operations. Students will be able control performance and optimization issues. It covers updating and upgrading of a database system. Students will be able to perform the importing and exporting of data to/from a database. Dual listed with IT 4310 (only one course may be taken for credit). **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Manage and organize data into a database. 2. Backup and restore a database. 3. Tune a database for better performance. 4. Import/export data to and from a database. Course fee required. Prerequisites: CS 4307 (Grade C or higher) OR IT 2300 (Grade C or higher). FA
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3.00 Credits
For students pursuing degrees in Computer Science or related fields, with an interest in the theory and practice of machine learning. Covers an introduction to supervised and unsupervised learning, including decision trees, neural networks, naive Bayes classifiers and support vector machines. Students will be required to implement machine learning systems. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Use supervised and unsupervised learning techniques. 2. Implement software learning systems. 3. Evaluate quality of learned systems. 4. Implement software utilizing the results of learning systems. Course fee required. Prerequisites: CS 2420 (Grade C or higher); AND CS 2810 (Grade C or higher); AND CS 3005 (Grade C or higher). SP
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3.00 Credits
This course explores the fundamental algorithms and statistics commonly used to extract patterns from large datasets. Students will practice discovering, cleaning, transforming, and exploring real datasets, actively applying the concepts discussed in the course using common programming tools. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Construct automated solutions to discover and extract patterns from large datasets. 2. Describe common data mining algorithms such as clustering and regression. 3. Apply strategies for large datasets such as streaming and sampling. Prerequisites: CS 2420 AND CS 2810 (Grade C or higher). FA
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3.00 Credits
This course covers the fundamentals of data visualization, including the types of visualizations and techniques to accommodate different types of data. Students will use state-of-the-art programming tools to practice communicating the meaning of patterns, anomalies, and insights found in real datasets. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Compare and contrast common types and approaches of data visualization. 2. Justify the use of appropriate visualization approaches for different types of datasets. 3. Create visualizations that effectively communicate insights from large datasets. Prerequisites: CS 2420 AND CS 2810 (Grade C or higher). FA
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3.00 Credits
Required of students pursuing a Computer Science degree or emphasis. Covers compiler design and implementation, including lexical analysis, parsing, symbol table management, and generating code through challenging programming assignments. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Create Grammars and Finite Automata corresponding to Regular Languages. 2. Scan a text file (of programming code) using a Finite Automata to identify the Tokens. 3. Parse a sequence of Tokens using Context Free Grammar to build a Parse Tree. 4. Interpret a Parse Tree to run the original programming code. 5. Write and execute machine code corresponding to a Parse Tree. 6. Build and maintain a Symbol Table to keep track of all variables in programming code. Course fee required. Prerequisites: CS 2420 (Grade C or higher); AND CS 2810 (Grade C or higher); AND CS 3005 (Grade C or higher). SP
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3.00 Credits
Required of students pursuing a Computer Science degree or emphasis. Students will complete an aggressive programming project of software engineering. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Have practical experience in project specification. 2. Have practical experience in project design. 3. Have practical experience in project implementation. 4. Have practical experience in project testing. Course fee required. Prerequisite: Advanced Standing. SP
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1.00 - 3.00 Credits
For Computer Science students who wish to engage in an undergraduate research project. Students will meet weekly with their faculty mentor to discuss progress on their project and receive feedback. At the end of the semester, the student will submit a written defense of their work. Students who expect to apply to graduate school should strongly consider taking this course. **COURSE LEARNING OUTCOMES (CLOs) At the successful conclusion of this course, students will be able to: 1. Survey state-of-the-art research. 2. Identify relevant research problems. 3. Articulate research goals by formulating a research problem and developing a research plan. 4. Demonstrate proficiency in Executing a research plan and defending the resulting contributions. Prerequisites: CS 2420 (Grade C or higher); AND CS 2810 (Grade C or higher). FA, SP
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